
# importing the required libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

import warnings
warnings.filterwarnings('ignore')


#Uploading database
data = pd.read_csv('database_IND.csv')


data.shape

(907, 27)


data.head()


data.isnull().sum()

country                       0
country_long                  0
name                          0
gppd_idnr                     0
capacity_mw                   0
latitude                     46
longitude                    46
primary_fuel                  0
other_fuel1                 709
other_fuel2                 906
other_fuel3                 907
commissioning_year          380
owner                       565
source                        0
url                           0
geolocation_source           19
wepp_id                     907
year_of_capacity_data       388
generation_gwh_2013         907
generation_gwh_2014         509
generation_gwh_2015         485
generation_gwh_2016         473
generation_gwh_2017         467
generation_gwh_2018         459
generation_gwh_2019         907
generation_data_source      458
estimated_generation_gwh    907
dtype: int64


data.describe()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 907 entries, 0 to 906
Data columns (total 27 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   country                   907 non-null    object 
 1   country_long              907 non-null    object 
 2   name                      907 non-null    object 
 3   gppd_idnr                 907 non-null    object 
 4   capacity_mw               907 non-null    float64
 5   latitude                  861 non-null    float64
 6   longitude                 861 non-null    float64
 7   primary_fuel              907 non-null    object 
 8   other_fuel1               198 non-null    object 
 9   other_fuel2               1 non-null      object 
 10  other_fuel3               0 non-null      float64
 11  commissioning_year        527 non-null    float64
 12  owner                     342 non-null    object 
 13  source                    907 non-null    object 
 14  url                       907 non-null    object 
 15  geolocation_source        888 non-null    object 
 16  wepp_id                   0 non-null      float64
 17  year_of_capacity_data     519 non-null    float64
 18  generation_gwh_2013       0 non-null      float64
 19  generation_gwh_2014       398 non-null    float64
 20  generation_gwh_2015       422 non-null    float64
 21  generation_gwh_2016       434 non-null    float64
 22  generation_gwh_2017       440 non-null    float64
 23  generation_gwh_2018       448 non-null    float64
 24  generation_gwh_2019       0 non-null      float64
 25  generation_data_source    449 non-null    object 
 26  estimated_generation_gwh  0 non-null      float64
dtypes: float64(15), object(12)
memory usage: 191.4+ KB


sns.countplot(x='primary_fuel', data=data)

<AxesSubplot:xlabel='primary_fuel', ylabel='count'>


sns.distplot(data['capacity_mw'])

<AxesSubplot:xlabel='capacity_mw', ylabel='Density'>


sns.boxplot(x='primary_fuel', y='capacity_mw', data=data)

<AxesSubplot:xlabel='primary_fuel', ylabel='capacity_mw'>


# showing the most columns
pd.set_option('display.max_columns',None)


df=pd.read_csv('database_IND.csv')


temp=df.copy()


df.head(5)


df['source'].nunique()

191


df['geolocation_source'].nunique()

3


print(" we have ",df.shape[0],'- Rows and ',df.shape[1],'-Columns')

 we have  907 - Rows and  27 -Columns


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 907 entries, 0 to 906
Data columns (total 27 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   country                   907 non-null    object 
 1   country_long              907 non-null    object 
 2   name                      907 non-null    object 
 3   gppd_idnr                 907 non-null    object 
 4   capacity_mw               907 non-null    float64
 5   latitude                  861 non-null    float64
 6   longitude                 861 non-null    float64
 7   primary_fuel              907 non-null    object 
 8   other_fuel1               198 non-null    object 
 9   other_fuel2               1 non-null      object 
 10  other_fuel3               0 non-null      float64
 11  commissioning_year        527 non-null    float64
 12  owner                     342 non-null    object 
 13  source                    907 non-null    object 
 14  url                       907 non-null    object 
 15  geolocation_source        888 non-null    object 
 16  wepp_id                   0 non-null      float64
 17  year_of_capacity_data     519 non-null    float64
 18  generation_gwh_2013       0 non-null      float64
 19  generation_gwh_2014       398 non-null    float64
 20  generation_gwh_2015       422 non-null    float64
 21  generation_gwh_2016       434 non-null    float64
 22  generation_gwh_2017       440 non-null    float64
 23  generation_gwh_2018       448 non-null    float64
 24  generation_gwh_2019       0 non-null      float64
 25  generation_data_source    449 non-null    object 
 26  estimated_generation_gwh  0 non-null      float64
dtypes: float64(15), object(12)
memory usage: 191.4+ KB


df[df['latitude'].isnull()].head(45)


df[df['name']=='Abhijeet']


df.describe().T


df.columns

Index(['country', 'country_long', 'name', 'gppd_idnr', 'capacity_mw',
       'latitude', 'longitude', 'primary_fuel', 'other_fuel1', 'other_fuel2',
       'other_fuel3', 'commissioning_year', 'owner', 'source', 'url',
       'geolocation_source', 'wepp_id', 'year_of_capacity_data',
       'generation_gwh_2013', 'generation_gwh_2014', 'generation_gwh_2015',
       'generation_gwh_2016', 'generation_gwh_2017', 'generation_gwh_2018',
       'generation_gwh_2019', 'generation_data_source',
       'estimated_generation_gwh'],
      dtype='object')


df.isnull().sum()

country                       0
country_long                  0
name                          0
gppd_idnr                     0
capacity_mw                   0
latitude                     46
longitude                    46
primary_fuel                  0
other_fuel1                 709
other_fuel2                 906
other_fuel3                 907
commissioning_year          380
owner                       565
source                        0
url                           0
geolocation_source           19
wepp_id                     907
year_of_capacity_data       388
generation_gwh_2013         907
generation_gwh_2014         509
generation_gwh_2015         485
generation_gwh_2016         473
generation_gwh_2017         467
generation_gwh_2018         459
generation_gwh_2019         907
generation_data_source      458
estimated_generation_gwh    907
dtype: int64


# % of data missing

round((df.isnull().sum()/df.shape[0])*100,2).sort_values(ascending=False)

estimated_generation_gwh    100.00
other_fuel3                 100.00
wepp_id                     100.00
generation_gwh_2013         100.00
generation_gwh_2019         100.00
other_fuel2                  99.89
other_fuel1                  78.17
owner                        62.29
generation_gwh_2014          56.12
generation_gwh_2015          53.47
generation_gwh_2016          52.15
generation_gwh_2017          51.49
generation_gwh_2018          50.61
generation_data_source       50.50
year_of_capacity_data        42.78
commissioning_year           41.90
latitude                      5.07
longitude                     5.07
geolocation_source            2.09
country                       0.00
url                           0.00
country_long                  0.00
primary_fuel                  0.00
capacity_mw                   0.00
gppd_idnr                     0.00
name                          0.00
source                        0.00
dtype: float64


plt.figure(figsize=(15,10))
sns.heatmap(df.isnull(),annot=True)

<AxesSubplot:>


#It would be unnecessary to use these features because % columns contain 100% missing data. It would be better to drop them.
#Other_fuel2 has 99.89% missing values, making it unnecessary to use these features. It is therefore preferable to remove them.
#other_fuel1 has 78.17% missing data, making it unnecessary to use this feature. It is therefore preferable to remove it.


df['other_fuel2'].unique()

array([nan, 'Oil'], dtype=object)


df['other_fuel1'].unique()

array([nan, 'Oil', 'Cogeneration', 'Gas'], dtype=object)


df['other_fuel1'].mode()

0    Oil
Name: other_fuel1, dtype: object


df['other_fuel1'].value_counts()

Oil             195
Gas               2
Cogeneration      1
Name: other_fuel1, dtype: int64


df.drop(['estimated_generation_gwh','other_fuel3','wepp_id','generation_gwh_2013','generation_gwh_2019'], axis=1, inplace= True)


df.shape

(907, 22)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 907 entries, 0 to 906
Data columns (total 22 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   country                 907 non-null    object 
 1   country_long            907 non-null    object 
 2   name                    907 non-null    object 
 3   gppd_idnr               907 non-null    object 
 4   capacity_mw             907 non-null    float64
 5   latitude                861 non-null    float64
 6   longitude               861 non-null    float64
 7   primary_fuel            907 non-null    object 
 8   other_fuel1             198 non-null    object 
 9   other_fuel2             1 non-null      object 
 10  commissioning_year      527 non-null    float64
 11  owner                   342 non-null    object 
 12  source                  907 non-null    object 
 13  url                     907 non-null    object 
 14  geolocation_source      888 non-null    object 
 15  year_of_capacity_data   519 non-null    float64
 16  generation_gwh_2014     398 non-null    float64
 17  generation_gwh_2015     422 non-null    float64
 18  generation_gwh_2016     434 non-null    float64
 19  generation_gwh_2017     440 non-null    float64
 20  generation_gwh_2018     448 non-null    float64
 21  generation_data_source  449 non-null    object 
dtypes: float64(10), object(12)
memory usage: 156.0+ KB


df['country'].unique()

array(['IND'], dtype=object)


df['country'].nunique()

1


sns.countplot(df['country'])

<AxesSubplot:xlabel='country', ylabel='count'>


#Because it just provides one piece of information, this column is unnecessary.Dropping this


df['country_long'].unique()

array(['India'], dtype=object)


df['country_long'].nunique()

1


sns.countplot(df['country_long'])

<AxesSubplot:xlabel='country_long', ylabel='count'>


#This column is unnecessary because it only provides one sort of information.We are dropping this


df['geolocation_source'].unique()

array(['National Renewable Energy Laboratory', 'WRI', nan,
       'Industry About'], dtype=object)

sns

<module 'seaborn' from 'C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\seaborn\\__init__.py'>


df['name'].unique()

array(['ACME Solar Tower', 'ADITYA CEMENT WORKS',
       'AES Saurashtra Windfarms', 'AGARTALA GT', 'AKALTARA TPP',
       'AKRIMOTA LIG', 'ALIYAR', 'ALLAIN DUHANGAN', 'ALMATTI DAM',
       'AMAR KANTAK', 'AMARAVATI TPP', 'ANANDPUR SAHIB ST-I&II',
       'ANAPARA "C"', 'ANDHRA', 'ANOOPGARH ST I&II', 'ANPARA', 'ANTA GT',
       'ANUPPUR', 'ANUPUR TPP', 'ARVINDNAGAR SUGAR', 'ASHOKNAGAR MILL',
       'ATHANI SUGAR', 'AURAIYA GT', 'AVANTHA BHANDAR TPP',
       'AWARPUR CEMENT PLANT', 'Abhijeet', 'Amarsar Solar Power Plant',
       'Anabura Wind Farm', 'Ananthapur - Meil Solar Power Plant',
       'Andrha Lake Wind Farm', 'Anikkadavu Wind Farm',
       'Arasinagundi Wind Farm', 'Askandra - DREPL Solar Power Plant',
       'Askandra - Electromech Maritech Solar Power Plant',
       'Askandra - Finehope Solar Power Plant',
       'Askandra - Khaya Solar Power Plant',
       'Askandra - Newton Solar Power Plant',
       'Askandra - Saidham Solar Power Plant',
       'Askandra - Vasavi Solar Power Plant', 'BADARPUR', 'BAGALKOT ICP',
       'BAGLIHAR HEP', 'BAGLIHAR HEP-II', 'BAIRA SIUL', 'BAKRESWAR',
       'BALCO TPP', 'BALIMELA', 'BALLARPUR BILT POWER', 'BAMANIA CEMENT',
       'BANDAKHAR TPP', 'BANDEL', 'BANER', 'BANSAGAR (I)',
       'BANSAGAR (II)', 'BANSAGAR (III)', 'BANSAGAR (IV)',
       'BANSWARA CEMENT', 'BARADARHA TPP', 'BARAMURA', 'BARAUNI', 'BARGI',
       'BARH STPP II', 'BARKHERA TPP', 'BARSINGAR LIGNITE',
       'BASIN BRIDGE GT', 'BASPA', 'BASSI', 'BCPL Wind Farm',
       'BEL Chikhalhol Wind Farm', 'BELA NAGPUR', 'BELGAUM UGAR',
       'BELLAD-BAGEWADI', 'BELLARY DG', 'BELLARY HOTHUR', 'BELLARY JANKI',
       'BELLARY TPS', 'BHADRA', 'BHADRAVATI MILL', 'BHAKRA',
       'BHANDARA STEEL MILL', 'BHANDARA VARAM', 'BHANDARDHARA',
       'BHATGARH', 'BHATSA', 'BHAVNAGAR TPP', 'BHAWANI KATTALAI -II',
       'BHAWANI KATTALAI -III', 'BHAWANI KATTALAI BARRAGE',
       'BHIGWAN MILL', 'BHILAI TPP', 'BHIRA', 'BHIRA TAIL RACE',
       'BHIVPURI', 'BHUSAWAL', 'BILIYAKALAN PLANT', 'BINA REFINERY',
       'BINA TPP', 'BINANI CEMENT PLANT', 'BINJKOTE', 'BINWA',
       'BIRSINGHPUR', "BOKARO A ''EXP''", 'BOKARO B', 'BONGAIGAON TPP',
       'BP Brahmanvel Wind Farm', 'BRAMHAPURAM DG', 'BSES Kerala CCGT',
       'BUDGE BUDGE', 'BUDHIL', 'BUTIBORI PLANT', 'BUTIBORI TPP -II',
       'Bamani Solar Power Plant', 'Bamanwali Solar Power Plant',
       'Bannari Amman Spinning Mills Wind Power Project',
       'Bap - GIS 1-2 Solar Power Plant', 'Bap - GIS 3 Solar Power Plant',
       'Bap - OPG Solar Power Plant',
       'Bap - Punj Lloyd Solar Power Plant',
       'Bapodar - Hiraco Solar Power Plant',
       'Bapodar - MBEDL Solar Power Plant',
       'Bavadi Barsingha - Giriraj Solar Power Plant',
       'Bavadi Barsingha - LEPL Solar Power Plant', 'Belagau Wind Farm',
       'Belagwi Wind Turbines', 'Belgaum Gadag Wind Farm',
       'Belgaum Wind farm', 'Bera Wind Farm',
       'Bhadrada Solar Power Plant', 'Bhat Khedi Wind Farm',
       'Bhatkota Solar Power Plant', 'Bitta Solar Power Plant',
       'Boha Solar Power Plant', 'Bonada Solar Power Plant',
       'CHAKABURA TPP', 'CHAMERA II', 'CHAMERA-I', 'CHAMERA-III',
       'CHANDA CEMENT WORKS', 'CHANDERIA CEMENT WORKS',
       'CHANDERIA SMELTER', 'CHANDRAPUR GUPTA', 'CHANDRAPUR MANIKGHAR',
       'CHANDRAPURA', 'CHANDRAPUR_Coal', 'CHANJU-I', 'CHEMBUKADAVU-II',
       'CHENANI I to III', 'CHHABRA TPS', 'CHIBRO (YAMUNA)', 'CHILLA',
       'CHUTAK', 'CHUZACHEN', 'CORE GREEN SUGAR',
       'Chandrapur Solar Power Plant', 'Charanka Solar Power Plant',
       'Chhadavada Wind Farm', 'Chingerbandh Solar Power Plant', 'D.P.L.',
       'DADRI (NCTPP)', 'DADRI GT', 'DAE RAJASTHAN', 'DAHANU FRESNEL',
       'DAMODARAM SANJEEVAIAH', 'DANAPUR BMM', 'DAUND SUGAR',
       'DAVANGERE MILL', 'DEHAR', 'DERANG', 'DGEN MEGA CCCP', 'DHAKRANI',
       'DHALIPUR', 'DHANU', 'DHARIWAL INF TPP', 'DHAULI GANGA', 'DHOLPUR',
       'DHUVARAN CCPP', 'DIGHE WORKS', 'DIKCHU', 'DIMBE', 'DLF',
       'DNYANESHWAR MILL', 'DOLVI ISPAT', 'DONKARAYI', 'DOYANG',
       'DUDH GANGA', 'DUGGAVATHI MILL', 'DULHASTI', 'DURG SPONGE IRON',
       'DURGAPUR', 'DURGAPUR STEEL TPS', 'Dadri Solar Power Plant',
       'Dandeli Mill', 'Davangere Wind Farm', 'Deh Solar Power Plant',
       'Devgarh Wind Farm', 'Dhabla Sondhya Solar Power Plant',
       'Dhanera Solar Power Plant', 'Dhank - Aravali Solar Power Plant',
       'Dhursar', 'Diwakar', 'Dugar Solar Power Plant', 'E.G. CANAL',
       'ELDARI', 'EMCO Sonurle Wind Farm', 'ESSAR GT IMP.',
       'Enercon Babarzar Wind Farm', 'Enercon Chitradurga Wind Farm',
       'Enercon Sonoshi Wind Farm', 'Essel Mining Wind Farm',
       'FARAKKA STPS', 'F_BAD CCGT',
       'Frost International Palankottai Wind Farm', 'G.I.P.C.L. GT',
       'GACL Kutch Wind Farm', 'GADARWARA', 'GADEPAN WORKS', 'GAJ',
       'GAMA CCPP  Module - 1', 'GANDHAR GT', 'GANDHARBAL',
       'GANDHI NAGAR', 'GANDHI SAGAR', 'GANGUWAL', 'GAUTAMI CCCP', 'GEPL',
       'GFL Gudhepanchgani', 'GHANVI', 'GHAT PRABHA', 'GHATGHAR PSS',
       'GHTP (LEH.MOH.)', 'GHUGUS LMEL', 'GIRI BATA', 'GIWPP Telagi',
       'GMR WARORA TPP', 'GODAVARI GT', 'GOINDWAL SAHIB TPP',
       'GRACE INDUSTRIES', 'GREL CCPP (RAJAMUNDRY)', 'GULBARGA CEMENT',
       'GUMTI', 'Gadag Wind Farm', 'Gadna 1 Solar Power Plant',
       'Gadna 2 Solar Power Plant', 'Gandhinagar Solar Power Plant',
       'Garacharma Solar Power Plant', 'Godawari',
       'Godelai Solar Power Plant', 'Gondri Solar Power Plant',
       'Gorsar Wind Farm', 'Grewal Solar Power Plant', 'Gujurat Solar On',
       'Gulabpura Solar Power Plant', 'Gwal Pahari Solar Power Plant',
       'HALDIA', 'HAMPI', 'HAPURI MILL', 'HASDEOBANGO', 'HAVALGA SUGAR',
       'HAZIRA CCCP', 'HAZIRA-GSECL', 'HINGOLI MILL', 'HIRAKUD I&II',
       'HIRIYUR BIOMASS', 'HZL Gadag Wind Farm',
       'HZL Nandurbar Maharashtra', 'HZL Palladum Wind Farm',
       'HZL Parewar Wind Farm', 'HZL Samana Wind Farm', 'H_GANJ B',
       'Hirewaddatti Wind Farm', 'Horti Solar Power Plant', 'I.B.VALLEY',
       'I.P.GT', 'IDAMALAYAR', 'IDUKKI', 'IEPL  BELA TPP',
       'IL&FS Kaladeh Wind Farm', 'IND BARATH TPP',
       'INDIA POWER TPP(HALDIA  HIRANMAYE )', 'INDIRA SAGAR',
       'INDRA GANDHI STPP', 'ITPCL TPP', 'Ingaleshwar Wind Farm',
       'Isanpur Solar Power Plant', 'Itnal Solar Power Plant', 'J.SAGAR',
       'JAISALMER GODAWARI', 'JALDHAKA I&II', 'JALLIPPA KAPURDI TPP',
       'JAYKAYPURAM PLANT', 'JAYPEE BELA CEMENT', 'JAYPEE REWA CEMENT',
       'JAYPEE SIDHI CEMENT', 'JEGURUPADU GT', 'JOG', 'JOJBERA',
       'JORETHANG LOOP', 'JSW RATNAGIRI TPP',
       'Jafrabad Solar Power Plant', 'Jamuria Solar Power Plant',
       'Jangi Wind Farm', 'Jangi village wind farm', 'Jeloo Wind Farm',
       'Jodiya Wind Farm', 'K.N. Ram Kaltek Wind Farm', 'KADAMPARI',
       'KADANA', 'KADRA', 'KAHALGAON', 'KAIGA', 'KAKATIYA TPP', 'KAKKAD',
       'KAKRAPARA', 'KALINADI', 'KALINADI SUPA', 'KALISINDH', 'KALLADA',
       'KALLUR GRINDING', 'KAMAL SPONGE', 'KAMALANGA', 'KANHER',
       'KARAD MILL', 'KARAIKAL', 'KARBI LANGPI', 'KARCHAM WANGTOO',
       'KARGIL', 'KARUPPUR GT', 'KASAIPALLI', 'KASARGODE DG',
       'KASHANG INTEGRATED', 'KASHIPUR CCPP', 'KASRAWAD FACTORY',
       'KATGHORA TPP', 'KATHALGURI GT', 'KAWAI TPP', 'KAWAS GT',
       'KAYAM KULAM GT', 'KCSF SANGUR', 'KHADAVASLA I&II',
       'KHAMBERKHERA IPP', 'KHANDONG', 'KHARA', 'KHATIMA', 'KHODRI',
       'KHOPOLI', 'KHOPOLI WORKS', 'KISHENGANGA', 'KM DODDI SUGAR',
       'KODASALI', 'KODAYAR-I&II', 'KODERMA', 'KOJIKODE DG', 'KOLAGHAT',
       'KOLDAM', 'KOLHAPUR HEMARUS', 'KOLHAPUR KAGAL', 'KOLHAPUR KUMBHI',
       'KOLLEGAL SUGAR', 'KONASEEMA CCCP', 'KONDAPALLI GT', 'KOPILI',
       'KOPPA SUGAR', 'KOPPAL HKMP', 'KORADI', 'KORBA STPS', 'KORBA-EAST',
       'KORBA-WEST', 'KOSI', 'KOTA', 'KOTA COMPLEX', 'KOTESHWAR', 'KOTLA',
       'KOVILKALAPPAL', 'KOYNA COMPLEX', 'KS Oils Chinnakuyili Wind Farm',
       'KUDGI', 'KULHAL', 'KUNDAH I-V', 'KUNDANKULAM', 'KUNDARGI MILL',
       'KUNDARKI TPP', 'KUTCH LIG.', 'KUTHUNGAL', 'KUTTALAM GT',
       'KUTTIADI', 'KUTTIYADI Add ext', 'KVK Energy Solar Project',
       'KYMORE', 'KYREDEMKULAI', 'K_GUDEM', 'K_GUDEM NEW', 'K_KHEDA II',
       'Kaladonger Wind Farm', 'Kamuthi Solar Power Plant',
       'Karmaria Solar Power Plant', 'Karur Textile Park Wind Farm',
       'Kathauti 1 Solar Power Plant', 'Kathauti 2 Solar Power Plant',
       'Katol - Citra Solar Power Plant',
       'Katol - Sepset Solar Power Plant', 'Khadoda Solar Power Plant',
       'Khandala Solar Power Plant',
       'Khetusar - Northern Solar Power Plant',
       'Khetusar - Suryauday Solar Power Plant',
       'Khilchipur Solar Power Plant',
       'Khirsara - Unity Solar Power Plant',
       'Khirsara - Welspun Solar Power Plant', 'Khirwa Solar Power Plant',
       'Khori Wind Farm', 'Kohinoor Wind Farm',
       'Komal West Solar Power Plant', 'Kosa Wind Farm',
       'Kottamangalam Wind Farm', 'Kottathara Village Wind Farm',
       'Kudankulam', 'LAKHERI CEMENT PLANT', 'LAKWA GT', 'LALITPUR TPP',
       'LARA', 'LARGI', 'LEIMAKHONG DG', 'LIGANAMAKKI', 'LIKIM RO',
       'LITL Uthumalai', 'LNJP Bahla Basti Wind Farm', 'LODHIVALI',
       'LOKMANGAL SUGAR ETHANOL', 'LOKTAK', 'LOWER JHELUM',
       'LOWER JURALA', 'LOWER LAGYAP', 'LOWER METTUR', 'LOWER PERIYAR',
       'LOWER SILERU', 'LTML Wind Energy Project in Tamil Nadu',
       'LVS POWER DG', 'Lakhewali Solar Power Plant',
       'Lakhmirwala Solar Power Plant', 'Lallian Kalan Solar Power Plant',
       'Lalpur Wind Farm', 'Landewadi Wind Farm',
       'Lathi Solar Power Plant', 'Liberty Oil Satara Wind Farm',
       'M.A.P.P.', 'MACHKUND', 'MADHAVAMANTRI', 'MADHIKHEDA',
       'MAHADEV PRASAD STPP', 'MAHAN TPP', 'MAHATMA GANDHI TPP',
       'MAHATMA SUGAR', 'MAHI BAJAJ I&II', 'MAHUL REFINERY',
       'MAIHAR CEMENT PLANT', 'MAITHON', 'MAITHON RB TPP', 'MALANA',
       'MALLARPUR', 'MANERI BHALI', 'MANGALORE REFINERY', 'MANGAON CCPP',
       'MANGLAM CEMENT', 'MANI DPH', 'MANIKDOH', 'MANIKGARH CEMENT',
       'MANIYAR', 'MAQSOODPUR  IPP', 'MARATHA CEMENT PLANT', 'MARWA TPP',
       'MATATILLA', 'MEJA STPP', 'MEJIA', 'MEJIA TPS EXT',
       'MELAMARUTHUR (MUTHIARA)', 'METTUR', 'METTUR DAM',
       'METTUR TPS EXT', 'MIHAN TPP', 'MOHAMAD PUR', 'MOHARA',
       'MONARCHAK CCPP', 'MORDI PLANT', 'MOUDA STPS', 'MOYAGCHU', 'MOYAR',
       'MRMPL Mudari Wind Farm', 'MSPL Wind Farm', 'MUDHOL CEMENT',
       'MUKERIAN I -IV', 'MUL GRETA', 'MUMBAI BPCL', 'MUMBAI JINDAL',
       'MUMBAI ONGC', 'MUNDRA TPP', 'MUNDRA UMPP', 'MUNIRABAD',
       'MUNOLI DISTILLERY', 'MURBAD PLANT', 'MUZAFFARPUR', 'MYNTDU',
       'Maloshi Wind Farm', 'Mandrup - Giriraj Solar Power Plant',
       'Megha Solar Plant', 'Mirpur Kalan - Alianz Eco Solar Power Plant',
       'Mirpur Kalan - Nexgen Solux Solar Power Plant',
       'Mithapur Solar Power Plant', 'Mokal Wind Farm', 'Mokla Wind farm',
       'Mulshi Solar Power Plant',
       'Muradwala Dal Singh Solar Power Plant', 'N.A.P.S',
       'NABI NAGAR TPP', 'NAGARJUNA SAGAR', 'NAGARJUNA SAGAR   TPD',
       'NAGDA GRASIM', 'NAGOTHANE COMPLEX', 'NAGPUR GMT', 'NAGPUR MILL',
       'NAGPUR SPONGE IRON', 'NAMRUP GT', 'NARASINGPUR', 'NARAYANPUR',
       'NARIMANGLAM', 'NASIK', 'NASIK SINNAR', 'NASIK TPP  PH 1',
       'NATHPA JHAKRI', 'NAWAPARA', 'NEPANAGAR MILL', 'NEW UMTRU',
       'NEYVELI FST EXT', 'NEYVELI ST I', 'NEYVELI ST II',
       'NEYVELI TPS EXP -II', 'NEYVELI TPS(Z)', 'NIGRI',
       'NIMBAHERA JK CEMENT', 'NIMOO BAZGO', 'NIRGAJANI(Ganga Canal)',
       'NITIN PLANT', 'NIWARI TPP', 'NIZAM SAGAR',
       'NMDC Anehalu Wind Farm', 'NORTH CHENNAI',
       'NORTH CHENNAI EXTENSION', 'NSL Wind Farm', 'NURANANG',
       'N_SAGAR LBC', 'N_SAGAR RBC', 'Nagda Hills Wind Energy project',
       'Nagercoli Wind Farm', 'Naini Solar Power Plant',
       'Nallakonda Wind Farm', 'Nargund Wind Farm',
       'Nokh - GGEL Solar Power Plant',
       'Nokh - Precision Technik Solar Power Plant',
       'Nokha Daiya Solar Power Plant', 'NuPower Tirunelveli Wind Farm',
       'OBRA', 'OBRA-A', 'OMKARESHWAR', 'ONGC Jerat Wind Farm',
       'ONGC Kutch Wind Farm', 'P.NALLUR CCGT', 'PAGUTHAN', 'PAINAMPURAM',
       'PAITHON', 'PALATANA CCPP', 'PALLIVASAL', 'PAMPORE GT', 'PANCHET',
       'PANIPAT', 'PANNIAR', 'PAPANASAM', 'PARAS', 'PARBATI-III',
       'PARBHANI GANGAKHED', 'PARE', 'PARICHA', 'PARLI', 'PARSEN_S VALLE',
       'PATALGANGA', 'PATHADI TPS PH -I', 'PATHRI', 'PAWANA',
       'PEDDAPURAM CCGT(Samalkot)', 'PENCH', 'PENNA AHOBELAM', 'PERIYAR',
       'PIPAVAV CCCP', 'POCHAMPAD', 'PONG', 'PORINGALKUTTU',
       'PORINGALKUTTU L', 'PRAGATI CCCP -III', 'PRAGATI CCGT',
       'PRIYADARSHNI JURALA', 'PRYAGRAJ (BARA) TPP', 'PULICHINTALA',
       'PUNE MILL', 'PURULIA PSS', 'PYKARA', 'PYKARA ALIMATE',
       'Panandhro Solar Power Plant', 'Patan Taluka Wind Farm',
       'Patnaik Hatti Wind Farm', 'Patnaik Jamnagar Wind Farm',
       'Patodi Solar Power Plant', 'Phagi Solar Power Plant',
       'Pokhran FVE Solar Power Plant', 'Pokhran THE Solar Power Plant',
       'Powerica Jangi Vandjiya', 'Powerica Kutch Wind Farm',
       'Powerica Theni Wind Farm', 'Powerica Tirunelveli Wind Farm',
       'Pratapgarh Solar Power Plant', 'Precious Solar Power Plant',
       'Punjawa - Sikh Wala Solar Power Plant', 'R.A.P.S.', 'R.P.SAGAR',
       'RABRIYAWAS CEMENT', 'RADHANAGRI', 'RAGHUNATHPUR TPP PH-I',
       'RAICHUR', 'RAICHUR STEEL', 'RAIGARH TPP', 'RAIKHEDA',
       'RAJASHREE CEMENT', 'RAJGHAT', 'RAJGHAT (MP)',
       'RAJIV GANDHI  TPS HISAR', 'RAJPURA DARIBA MINE', 'RAJPURA TPP',
       'RAMANAGARAM MILL', 'RAMGANGA', 'RAMGARH GT', 'RAMMAM', 'RAMPUR',
       'RANGANADI', 'RANGIT-III', 'RANJANI MILL', 'RANJIT SAGAR',
       'RAS CEMENT PLANT', 'RATIJA TPP', 'RATNAGIRI GAS', 'RAYAL SEEMA',
       'RELIANCE ENERGY', 'RENGALI', 'RIHAND', 'RIHAND_Hydro',
       'RITHALA CCCP', 'RMC MANGROL', 'ROKHIA GT', 'ROPAR',
       'ROSA TPP PH - 1', 'RSMNL Wind Farm', 'R_GUNDEM - B',
       'R_GUNDEM STPS', 'Rajgarh - NTPC Solar Power Plant',
       'Rajgarh - Ujaas Solar Power Plant', 'Raksa Solar Power Plant',
       'Ram Tirath Jaga Solar Power Plant',
       'Ramagundam Solar Power Plant', 'Rapar Khokhara Solar Power Plant',
       'Rasulpur Solar Power Plant', 'Ratedi Mondri Wind Farm',
       'Ravra - IOC Solar Power Plant', 'Ravra - NWEPL Solar Power Plant',
       'Ravra - SEI Solar Power Plant',
       'Ravra - Welspun Solar Power Plant',
       'Relaxo Footwear Kui Inda Wind Farm',
       'Roaring 40s Wind Farms (Khandke) Private Limited - Phase II',
       'Rojhani Solar Power Plant', 'Rucha Soya Wind Farm',
       'Ruchi Soya Palsodi Wind Farm', 'S.SAROVAR CHPH', 'S.SAROVAR RBPH',
       'SABARIGIRI', 'SAGARDIGHI TPP', 'SAHAKAR SHIROMANI', 'SAINJ',
       'SALAL I & II', 'SALAYA TPP', 'SALORA', 'SAMALPATTI DG',
       'SAMAYANALLUR DG', 'SAMEERWADI MILL', 'SANGLI KRANTI',
       'SANGLI MIRAJ BIOMASS', 'SANGLI VASANTDADA', 'SANJAY BHABA',
       'SANJAY GANDHI', 'SANKESHWAR MILL', 'SANTALDIH', 'SARKARPATHY',
       'SASAN UMPP', 'SATNA CEMENT PLANT', 'SATPURA', 'SBT SUGAR',
       'SEIONI TPP', 'SEMBCORP GAYATRI', 'SENGULAM', 'SERVALAR',
       'SEWA-II', 'SEWA-III', 'SHAHPUR', 'SHANAN', 'SHARAVATHY',
       'SHARAVATHY TAIL RACE (Gerusupa)', 'SHIMSAPURA', 'SHIROL MILL',
       'SHIRPUR', 'SHIVAPURA', 'SHOLAYAR', 'SHOLAYAR I&II',
       'SHREE MEGA POWER', 'SHRI MALAPRABHA', 'SHRI SHANKAR',
       'SHRIRAM RAYONS PLANT', 'SIIL Jaibhim Wind Farm', 'SIKKA REP.',
       'SIMHADRI', 'SIMHAPURI TPP', 'SINGARENI TPP', 'SINGRAULI STPS',
       'SINGUR', 'SIPAT STPS', 'SITAPURAM POWER LIMITED', 'SIVASAMUNDRUM',
       'SOLAPUR STPP', 'SONAI MILL', 'SONE EAST CANAL', 'SONE WEST CANAL',
       'SOUTHERN REPL.', 'SRINAGAR', 'SRISAILAM', 'SRISAILAM LBPH',
       'STAKNA', 'STERLITE TPP', 'SUBERNREKHA I&II', 'SUGEN CCCP',
       'SURAT GARH', 'SURAT LIG.', 'SURATGARH', 'SURULIYAR', 'SURYA',
       'SVPL', 'SWASTIK KORBA', 'SWPPL Madhavakurichi',
       'SWPPL Manurpalayam', 'SWPPL Vadakkupanavadali',
       'Sadeipali - ASPL Solar Power Plant',
       'Sadeipali - REHPL Solar Power Plant', 'Sai Sulphonates Wind Mill',
       'Sakri 1A Solar Power Plant', 'Sakri 1B Solar Power Plant',
       'Sangatpura Solar Power Plant', 'Satara Wind Farm',
       'Shiloj Solar Power Plant', 'Shirsoli Solar Power Plant',
       'Shivasamundra Solar Power Plant',
       'Shivlakha - Backbone Solar Power Plant',
       'Shivlakha - CPEC Solar Power Plant',
       'Shivlakha - Konark Solar Power Plant', 'Shree Nashik Wind Farm',
       'Shree Sangli Wind Farm', 'Shree Southern Wind Farm',
       'Shri Singaji MALWA TPP', 'Singrauli Solar Power Plant',
       'Sivaganga Solar Power Plant', 'Sivalaperi Wind Farm',
       'Solitaire Solar Power Plant', 'Sonu Wind Farm',
       'Sterling Agro Akal Wind Farm', 'Sterling Agro Mokla Wind Farm',
       'Sterling Karnataka site', 'Sterling Madhya Pradesh site',
       'Sumilon Shikarpur Wind Farm', 'Sumilon Vershamedi Wind Farm',
       'Supe Solar Power Plant', 'Surajbari Creek Wind Farm', 'T.B. DAM',
       'TADALI', 'TADALI SPONGE IRON', 'TAGO', 'TALCHER', 'TALCHER STPS',
       'TALWANDI SABO', 'TAMNAR TPP', 'TANAKPUR', 'TANDA', 'TANIR BAVI',
       'TARAPUR', 'TASHIDING', 'TAWA', 'TEESTA  I-III', 'TEESTA -V',
       'TEESTA LOW DAM -IV', 'TEESTA LOW DAM-III', 'TEESTA-III',
       'TEHRI ST -1', 'TENUGHAT', 'THAL WORKS', 'THAMMINAPATNAM TPP',
       'THANE PLANT', 'THIROT', 'TILLARI', 'TILLAYA', 'TIRORA TPP',
       'TITAGARH', 'TORANGALLU EXT', 'TORANGALLU IMP', 'TORR POWER SAB.',
       'TROMBAY', 'TROMBAY GT', 'TROMBAY WORKS', 'TROMBAY_Coal',
       'TROMBAY_Oil', 'TUIRIAL', 'TUNGABHADRA SUGAR', 'TUTICORIN',
       'TUTICORIN JV', 'TUTICORIN- IND BARATH', 'TVS Kunnur Wind Farm',
       'TVS Tirunelveli Wind Farm', 'TVS Virali Wind Farm',
       'Tadas wind farm', 'Talcher Kaniha Solar Power Plant',
       'Tamilnadu Wind Farm',
       'Tata BP - Electronics City Solar Panel Plant',
       'Tata Power Gadag Plains Wind Farm', 'Tata Wind Farm',
       'Teona Pujarian Solar Power Plant', 'Theni Wind Farm',
       'Thimmapuram Wind Farm', 'Tinwari - MB Solar Power Plant',
       'Tiruppur', 'Tiruppur 2', 'Torrent Gujurat Wind Farm',
       'U.B.D.C. ST.-I& II', 'U.ROGNICHU', 'UCHPINDA TPP', 'UDUPI TPP',
       'UJJAINI', 'UKAI LBC', 'UKAI_Coal', 'UKAI_Hydro', 'UMIAM I II &IV',
       'UMTRU (NEW)', 'UNCHAHAR', 'UNO SUGEN', 'UPL Dharapuram Wind Farm',
       'UPPAR INDRAVATI', 'UPPER KOLAB', 'UPPER SILERU I&II',
       'UPPER SINDH I& II', 'URAN GT', 'URAN ONGC', 'URI', 'URI -II',
       'URUMI', 'UTRAN CCCP EXT', 'UTRAULA TPP', 'Udumalpet Wind Farm',
       'VAIGAI DAM', 'VAITARNA', 'VALANTHARVI GT', 'VALLUR ntpc/ntecl',
       'VALUTHUR GT', 'VARAHI', 'VARDHAM BUDNI', 'VARDHAM SATLAPUR',
       'VASAVADATTA CEMENT', 'VEMAGIRI CCCP', 'VENUNAGAR SUGAR',
       'VIJAIPUR WORKS', 'VIJAYWADA', 'VIJAYWADA TPP-IV', 'VIJESWARAM GT',
       'VIKRAM CEMENT', 'VINDH_CHAL STPS', 'VIR', 'VISHNU PRAYAG',
       'VIZAG TPP', 'VRL Wind Farm', 'Vaayu Ashti Wind Farm',
       'Vankuswade Wind Farm', 'Vastan Solar Power Plant',
       'WADI CEMENT PLANT', 'WANAKBORI',
       'WARDHA WARORA(Sai Wardha Power)', 'WARDHA WORKS', 'WARNA',
       'WY.CANAL A -D', 'Wadgam Solar Power Plant',
       'Warora Solar Power Plant', 'Weizzmann Forex Idukki Wind Farm',
       'Wind power project by Riddhi Siddhi Gluco Biols Limited (RSGBL)',
       'Wind power project in Rajasthan', 'YAMUNANAGAR TPP',
       'YASHWANTRAO MOHITE', 'YELHANKA (DG)', 'YERMARUS TPP',
       'Yelesandra Solar Power Plant', 'Yelisirur wind power project',
       'ZAWAR MINES', 'iEnergy Theni Wind Farm'], dtype=object)


df['name'].nunique()

907


sns.countplot(df['name'])

<AxesSubplot:xlabel='name', ylabel='count'>


df['other_fuel1'].nunique(dropna=False)

4


df['other_fuel1'].value_counts()

Oil             195
Gas               2
Cogeneration      1
Name: other_fuel1, dtype: int64


df['other_fuel2'].nunique()

1


df['other_fuel2'].unique()

array([nan, 'Oil'], dtype=object)


df['other_fuel2'].value_counts(dropna=False)

NaN    906
Oil      1
Name: other_fuel2, dtype: int64


#Even if we impute this with Mode, the feature will only include one type of data, which is irrelevant for model prediction.

# So dropping this


df.drop(['country','country_long','name','other_fuel1','other_fuel2'   ], axis=1,inplace=True)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 907 entries, 0 to 906
Data columns (total 17 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   gppd_idnr               907 non-null    object 
 1   capacity_mw             907 non-null    float64
 2   latitude                861 non-null    float64
 3   longitude               861 non-null    float64
 4   primary_fuel            907 non-null    object 
 5   commissioning_year      527 non-null    float64
 6   owner                   342 non-null    object 
 7   source                  907 non-null    object 
 8   url                     907 non-null    object 
 9   geolocation_source      888 non-null    object 
 10  year_of_capacity_data   519 non-null    float64
 11  generation_gwh_2014     398 non-null    float64
 12  generation_gwh_2015     422 non-null    float64
 13  generation_gwh_2016     434 non-null    float64
 14  generation_gwh_2017     440 non-null    float64
 15  generation_gwh_2018     448 non-null    float64
 16  generation_data_source  449 non-null    object 
dtypes: float64(10), object(7)
memory usage: 120.6+ KB


df['gppd_idnr'].nunique()

907


df['gppd_idnr']

0      WRI1020239
1      WRI1019881
2      WRI1026669
3      IND0000001
4      IND0000002
          ...    
902    IND0000513
903    WRI1026222
904    WRI1026776
905    WRI1019901
906    WRI1026761
Name: gppd_idnr, Length: 907, dtype: object


df['primary_fuel'].unique()

array(['Solar', 'Coal', 'Wind', 'Gas', 'Hydro', 'Biomass', 'Oil',
       'Nuclear'], dtype=object)


df['owner'].nunique()

280


df['source'].nunique()

191


df['url'].nunique()

304


df['url'].unique()

array(['http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=262',
       'http://www.ultratechcement.com/',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1328700673.83/view',
       'http://www.cea.nic.in/', 'http://www.hindustanpowerprojects.com/',
       'http://www.ambedkarsugar.com/',
       'http://www.mapsofindia.com/whitepages/foods-and-beverages/food-processors-and-manufacturers/ashokssklimited',
       'http://www.renukasugars.com/',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=254',
       'http://www.navbharatbuildcon.com/windmill--solar-plant.html',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1216117082.43/view',
       'http://www.meil.in/power-projects',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1348755977.79/view',
       'https://cdm.unfccc.int/Projects/DB/BVQI1379744232.36/viewhttps://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1278588174.29/view',
       'http://www.lancosolar.com/pdfs/rajasthan-pv-project-details.pdf',
       'http://www.icpluttur.com/', 'http://bilt.com/',
       'http://www.jkcement.com/', 'http://www.indiacements.co.in/',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1218300485.48/view',
       'https://cdm.unfccc.int/Projects/DB/RINA1416216844.8/view',
       'http://www.idealenergysolar.com/', 'http://www.ugarsugar.com/',
       'http://www.vssil.co.in/', 'http://hothurindia.com/',
       'http://jankicorp.com/', 'http://www.mpm.co.in/',
       'http://www.sunflagsteel.com/',
       'http://www.bloomberg.com/profiles/companies/4115076Z:IN-varam-bio-energy-pvt-ltd',
       'http://www.sangamgroup.com/', 'http://borl.in/',
       'http://binaniindustries.com/',
       'https://cdm.unfccc.int/filestorage/T/0/C/T0CJ85FSL1WN9VEXID67ZOBMQRGAPH/Revised%20PDD%20-%20Clean.pdf?t=UE58b2pkZGN1fDC4di78ksdJWNuCM4aKL4xu',
       'http://www.indoramaindia.com/',
       'http://www.ambedkarsugar.com/solar_power.php',
       'http://www.aewinfra.com/solar.html',
       'https://cdm.unfccc.int/filestorage/S/7/J/S7JBL0QFRHWA1DEZ2O4NUTG5Y6VKMP/PDD%20BASML%20Version%2003.pdf?t=Mmh8b2pxZWR0fDDj8zCpobgUeh2ry3t0r5N5',
       'http://www.sembcorp.com/en/media/features?Id=9179',
       'https://cdm.unfccc.int/filestorage/Jyh1M',
       'http://www.punjlloyd.com/energy/renewables',
       'http://www.hiracoindia.com', 'http://www.moserbaersolar.com',
       'http://sterlingandwilson.com/giriraj-enterprises-solar-rajasthan',
       'http://www.lepl.in',
       'https://cdm.unfccc.int/filestorage/7/l/SG76AFZB9EYTJ4321MOWIKQ8PCDNXR.pdf/9684%20PDD.pdf?t=emF8b2pibm5jfDCvkTnPwzrrv5OWSd79JKVI',
       'https://cdm.unfccc.int/filestorage/t/h/3ZTDL1JSY8F96X7WM05GQEOBHVCRKU.pdf/PDD_0.85%20MW%20x%202%20Wind?t=OUd8b2pibmtifDA3QWPDKTd9w1VzFuhHzr3p',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1204705646.68/view',
       'https://cdm.unfccc.int/filestorage/K/H/4/KH4609PBJU1SRCMD8W7FNQ2LTZGIO5/ENRE%20PDD%20version%205_0_21_12_2010%20Clean%20Mode.pdf?t=dWt8b2picnVxfDDdBloH5bI-0qA-0I--EtaE',
       'https://cdm.unfccc.int/filestorage/z/g/GHX720LOZTBENA6IVS1PJCWF8YMKUD.pdf/2347%20Revised%20PDD.pdf?t=YUh8b2picHN3fDCY1tP_B8ik_E25vGal7UR-',
       'http://www.lancosolar.com',
       'https://cdm.unfccc.int/filestorage/h/l/W9FUXHOTR0Y5V2KQBIS4MPN17JEZLC.pdf/PDD%20-%2098.7%20MW%20wind%20power%20project%20at%20Ratlam%20in%20Madhya%20Pradesh%20by%20DJ%20Energy%20Private%20Limited.pdf?t=R2x8b2picWpsfDACMivhrOTU62i3hfmcbopg',
       'http://www.abelloncleanenergy.com', 'http://www.adanipower.com',
       'http://www.madhavcorp.com',
       'http://www.rrecl.com/PDF/Details%20of%20Rajasthan.pdf',
       'http://www.acclimited.com/', 'http://www.birlacorporation.com/',
       'http://www.hzlindia.com/', 'http://guptacoal.com/',
       'http://www.manikgarhcement.com/', 'http://www.coregreen.in/',
       'http://mahagenco.in',
       'https://natgrp.org/charanka-gujarat-solar-park/',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1304071464.49/view',
       'http://www.p3green.com', 'http://www.reliancepower.co.in/',
       'http://jobs.cari.co.in/jobs/bmm-ispat-ltd',
       'http://www.daundsugar.com/',
       'http://biomass-power.industry-focus.net/karnataka-biomass-projects/148-davangere-sugar-co-to-set-up-30-mw-biomass-power-project-davangere-sugar-co-to-set-up-30-mw-biomass-power-project.html',
       'http://www.mukand.com/', 'http://dssk.co.in/',
       'http://www.jsw.in/',
       'http://www.tradeindia.com/Seller-5380843-SHAMANUR-SUGARS-LIMITED/',
       'https://www.jaibalajigroup.com/power.html',
       'http://www.ntpc.co.in',
       'http://www.westcoastpaper.com/infrastructure/',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1142448670.58/view',
       'https://natgrp.files.wordpress.com/solar-power-rajasthan-upto-march-2013.pdf',
       'https://cdm.unfccc.int/filestorage/Y/I/R/YIR8M4NQJKLW63H1OZCGBTSA729PU5/37.5_MW_PDD_-_37.5_MW_Wind_power_project_at_Pratapgarh%2C_Rajasthan?t=WTF8b2pkZHAyfDApG31asZJ6b9TuTgaPGJDE',
       'http://ujaas.com', 'http://www.firstsolarind.com',
       'http://www.aipl.in',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=272',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=258',
       'http://www.rrecl.com/PDF/Details%20of20Rajasthan.pdf',
       'https://cdm.unfccc.int/Projects/DB/PJR%20CDM1328594375.93/view',
       'https://cdm.unfccc.int/Projects/DB/URSCert1371557111.17/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1185356859.49/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1279516994.31/view',
       'https://cdm.unfccc.int/filestorage/O/6/L/O6LOWTE60VD7BDDY8ZB8AQBVNQQZCJ/1115%20PDD%20revised.pdf?t=dWd8b2picW9kfDBLXNNinuA2EARAJAJta6Yw',
       'https://cdm.unfccc.int/Projects/DB/BVQI1354554757.67/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1346053136.1/view',
       'http://chambalfertilisers.com/',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1202913883.06/view',
       'http://www.lloyds.in/',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1323884772.8/view',
       'http://www.graceindustries.com/', 'http://www.orientcement.com/',
       'https://cdm.unfccc.int/Projects/DB/BVQI1239021527.94/view',
       'http://sunborneenergy.com', 'http://www.pdpu.ac.in',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=247',
       'http://mnre.gov.in/file-manager/UserFiles/State%20wise%20commissioning%20status%20of%20grid%20connected%20solar%20power%20projects/Rajasthan.pdf',
       'http://energy.rajasthan.gov.in/content/dam/raj/energy/common/Details%20of%20commissioned%20Solar%20Projects%20.pdf',
       'https://cdm.unfccc.int/filestorage/d/d/56KO8FQND07Y3H12PBALXWG9MRC4JV.pdf/PDD_-_GMDC_V05.pdf?t=bGh8b2pkZDEwfDAxL3Mrj_s1ZpnXL5ljSdCb',
       'https://www.google.com/maps/d/embed?mid=1bXiXRcLRnBCUfpkHM2J5EVryhG4&ll=31.06035315253383%2C35.02284616406246&z=8',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=263',
       'http://www.rrecl.com/PDF/Details%0Rajasthan.pdf',
       'http://www.mnre.gov.in',
       'http://www.shamrajenercon.com/cer16.htm',
       'http://www.purnasakhar.com/', 'http://rkmpowergen.in/',
       'https://cdm.unfccc.int/Projects/DB/BVQI1208874936.63/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1351776938.73/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1352807242.64/view',
       'https://cdm.unfccc.int/filestorage/x/_/BYO3D0NJE4W2ZULSAXTQC61IM5HV9R.pdf/7873-PDD-25%20Oct%2012.pdf?t=ZGh8b2pkZ3A5fDB5_GTLkYcWdSL2xW6rf1F3',
       'https://cdm.unfccc.int/Projects/DB/BVQI1211956663.14/view',
       'https://cdm.unfccc.int/filestorage/s/x/8LZ62R4VIOK570F3AWSBNTUECYJ9XD.pdf/3_Revsied_PDD_JAL_2013_01_08_Clean_mode.pdf?t=VkZ8b2pkY2xwfDD7-5fVv-HnyeV-IOsi3cee',
       'http://www.esselgroup.com',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1346322352.66/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1351166669.28/view',
       'http://harshaengineers.com', 'http://karnatakapower.com',
       'http://hiragroup.com/companies/godawari-green-energy-limited/',
       'http://www.jklakshmicement.com/',
       'http://www.jalindia.com/subsidiaries.html',
       'http://www.jinkosolar.com/press_detail_568.html?lan=en',
       'http://wbpdcl.co.in',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1342443620.03/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1279520653.8/view',
       'https://cdm.unfccc.int/Projects/DB/BVQI1312546277.77/view',
       'https://cdm.unfccc.int/Projects/DB/BVQI1351922842.98/view',
       'http://www.chettinad.com/', 'http://www.kjsahluwaliagroup.com/',
       'http://www.kcpsugar.com/index.html', 'http://maraloverseas.com/',
       'http://www.gmsugars.com/', 'http://www.uttamgalva.com/',
       'http://www.chamundeswarisugars.in/',
       'http://www.hemarus.co.in/facilities.html',
       'http://www.tradeindia.com/Seller-6835496-Shri-Dudhganga-Vedganga-SSK-Ltd-/',
       'http://www.kumbhisugar.com/', 'http://www.bannari.com/sugar.html',
       'http://www.nslsugars.com/',
       'http://www.tradeindia.com/Seller-2020295-HARE-KRISHNA-METALLICS-PVT-LTD-/',
       'http://www.dcmshriram.com/',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1300103078.33/view',
       'http://www.gemgroup.in/gem-sugar.html',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=260',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1356681143.52/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1297334588.3/view',
       'http://www.azurepower.com',
       'http://www.mahaurja.com/PDF/PG2_GridConnSPPCommissioned.pdf',
       'http://www.solairedirect.com', 'http://www.acme.in',
       'http://www.backboneworld.com', 'http://www.welspunrenewables.com',
       'http://www.rrecl.com/PDF/Details%20of%Rajasthan.pdf',
       'https://cdm.unfccc.int/filestorage/L/V/Q/LVQ9ANSCJX8164E5H7PWYIB3O0RZ2U/Revised%20PDD%20Version%2006%20dated%205th%20June%202012%20-%20Clean%20mode.pdf?t=akx8b2pxZm9ufDC6M3RsDeQKejKPRFouqWsR',
       'https://cdm.unfccc.int/Projects/DB/BVQI1302691944.71/view',
       'http://www.tatapowersolar.com',
       'https://cdm.unfccc.int/filestorage/w/m/64TXH0Y1V9ZCISBKO3F758PEQNUJDR.pdf/PDD__V-2_5_19_10_2012.pdf?t=akh8b2pkZTFtfDAP0pu4sjZSao0P-GV-Qzqn',
       'https://cdm.unfccc.int/filestorage/a/1/0JPNG5S4A821KD6IUHWBOEYRVFTL3X.pdf/ALWE%20PDD_Ver%206%20_06.03.2013_%20Clean%20.pdf?t=dlV8b2picjMyfDAQ_TbHluMZz9jMHYOK8XbU',
       'https://cdm.unfccc.int/Projects/DB/BVQI1315830379.2/view',
       'https://www.iaea.org/PRIS/CountryStatistics/ReactorDetails.aspx?current=853',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1200600517.28/view',
       'https://cdm.unfccc.int/Projects/DB/KBS_Cert1356901378.31/view',
       'http://www.tatapower.com/', 'http://lokmangal.com/site/agro.html',
       'https://cdm.unfccc.int/Projects/DB/RINA1359562234.41/view',
       'http://www.peda.gov.in/main/SPVPowerProjects.html',
       'http://esselinfraprojects.com',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1354282051.48/view',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1335362700.21/view',
       'http://www.lancogroup.com',
       'https://cdm.unfccc.int/Projects/DB/Applus1436171071.3/view',
       'https://www.zaubacorp.com/company/MAHATMA-SUGAR-POWER-LIMITED/U15421MH1997PLC111855',
       'https://bharatpetroleum.com/', 'http://www.maiharcement.co.in/',
       'https://mrpl.co.in/', 'http://www.mangalamcement.com/',
       'http://www.ambujacement.com/', 'http://rswm.in/',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1278588174.29/view',
       'https://cdm.unfccc.int/filestorage/I/M/8/IM82KOV79XD6W1FSRY0APUBQT5HNG3/PDD%20Version%203.1?t=eXJ8b2pkY3A3fDDe6Zq2yZtRMfXDrbXuYkLx',
       'http://www.jkcement.com/power-plants',
       'http://www.gretagroup.com/', 'http://www.ongcindia.com/',
       'http://www.technocraftgroup.com/',
       'https://cdm.unfccc.int/filestorage/A/R/Q/ARQ8TKW54JBLF1U3E6OXVHCDSN9M70/PDD-CEM-0792-27-09.pdf?t=V0d8b2pibmNpfDBisKvrZGRCvGuhGr5W8zZF',
       'http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=264',
       'https://cdm.unfccc.int/filestorage/o/t/LOE8KY3952J1UZAFN6CHMPQBVD7RS0.pdf/PDD_RSMML.pdf?t=QUZ8b2pkZzRzfDAgGCgKw2iEeb78bqr1DaV0',
       'https://cdm.unfccc.int/filestorage/_/r/F932EYQBPVM7IXZ4KWRUG5HT6S0ACL.pdf/7895-PDD-26%20Oct%2012.pdf?t=aEJ8b2picjhvfDAyiJ_x---be015MnlJwvjn',
       'https://www.solairedirect.com', 'http://www.grasim.com/',
       'http://www.ril.com/OurBusinesses/Petrochemicals.aspx',
       'http://purtigroup.com/gmt-mining.html', 'http://purtigroup.com/',
       'http://www.topworthgroup.com/',
       'http://www.orientgreenpower.com/', 'http://www.rattanindia.com/',
       'http://www.epw.in/journal/1962/42/chair-uncategorised/national-newsprint-and-paper-mills-ltd-nepanagar-mp.html',
       'http://www.moneycontrol.com/india/stockpricequote/cement-major/jkcement/JKC03',
       'http://www.nitinspinners.com/',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1323884913.69/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1173772302.89/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1135356510.37/view',
       'https://cdm.unfccc.int/filestorage/5/D/0/5D0I7GCWXZ1VFNS6ROQKHTMYL8JA39/Nagercoil%20PDD%20Corrected_c.pdf?t=bkl8b2pkZGlofDAiW_GZYG7HXm3tF_Ie-IsH',
       'http://www.emcpower.com',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1355495522.4/view',
       'https://cdm.unfccc.int/Projects/DB/PJR%20CDM1353582075.03/view',
       'http://hiragroup.com',
       'https://cdm.unfccc.int/filestorage/3/x/NUSA0ZGPJRT6YXIMEVOB74WDHQ5KF8.pdf/Final%20PDD.pdf?t=QzV8b2picHExfDDzqlVY2T6pSjzHeaKJVjyC',
       'https://cdm.unfccc.int/filestorage/L/M/Z/LMZG56NYST19Q7IHD4EPXJRF2VB03W/PDD.pdf?t=R1p8b2picTYxfDApzXyA-gGP8NbbOgKvi68C',
       'https://cdm.unfccc.int/filestorage/F/X/W/FXWNHT2DU5RV3BGL7YA8ZKPI46OS9E/2856_PDD_clean.pdf?t=RGp8b2picmp5fDB2P7QGVKqmOvLt7pNBaIqW',
       'http://www.gangakhedicpp.com/', 'http://www.ril.com/',
       'http://www.sinarmas.com/en/business-units/',
       'http://www.gmdcltd.com',
       'https://cdm.unfccc.int/Projects/DB/SIRIM1355276782.28/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1306214743.43/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1288029478.94/view',
       'http://www.reliancepower.co.in',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1411994965.49/view',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1270819651.34/view',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1300097036.88/view',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1264590823.08/view',
       'http://icmlindia.com', 'http://www.suranaind.com/',
       'http://www.mapsofindia.com/whitepages/foods-and-beverages/food-processors-and-manufacturers/sprsugars',
       'http://naturalsugar.in/', 'http://www.shreecement.in/',
       'https://cdm.unfccc.int/Projects/DB/BVQI1201770524.09/view',
       'http://astonfield.com', 'http://taxusgroup.com',
       'https://cdm.unfccc.int/Projects/DB/RINA1354700454.05/view',
       'https://www.iocl.com',
       'http://sterlingandwilson.com/northwest-energy-pvt-ltd',
       'http://ifcext.ifc.org/ifcext/91',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1316100537.64/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1263981578.63/view',
       'https://cdm.unfccc.int/filestorage/z/4/H6BE40Q3YPZ7ULD8OWFR1AIT5V2SXM.pdf/8592%20PDD.pdf?t=Sjd8b2pkZHV0fDDHMHx5kl40-cCl4qrFlA08',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1285157251.43/view',
       'http://www.sitsonindia.com/pdf/EPC%20power%20projects.pdf',
       'http://www.somaiya.com/',
       'http://food.industry-focus.net/sugar/379-kranti-ssk-to-expand-its-sugar-mill-in-kundal-village-kranti-ssk-to-expand-its-sugar-mill-in-kundal-village.html',
       'https://cdm.unfccc.int/Projects/redirector?ref=4063',
       'http://viainfotech.biz/Biomass/theme5/document/green_market/REC-project-list.pdf',
       'http://www.belgaum.nic.in/english/Heerasugarfactory.html',
       'http://www.thoratsugar.com/', 'http://dattasugar.co.in/',
       'http://www.mapsofindia.com/whitepages/foods-and-beverages/wine-and-beer/shrimassk',
       'http://www.dcmsr.com/',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1340102581.62/view',
       'https://cdm.unfccc.int/Projects/DB/BVQI1351312018.91/view',
       'http://www.cdmindia.gov.in/project_details_view.php?id=1511',
       'http://rehpl.co.in',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1318484242.75/view',
       'https://cdm.unfccc.int/filestorage/0/5/A/05AYCZFT4LWMHD216PU9SV7XO8BENR/PDD%20Version%201.2?t=U2Z8b2picWJsfDAiEl4MxIcSh1urnyFbvIy_',
       'http://www.monosteel.in',
       'http://www.jains.com/Solar/Renewable%20energy.htm',
       'http://www.madhavgrp.co.in/SJ_solar.html',
       'http://www.konarkgroup.co.in',
       'https://cdm.unfccc.int/filestorage/D/O/R/DORZF791H8K4NWACPQ5IMTUYLG2VXE/CDM_PDD_v6.pdf?t=WGN8b2sxbDRnfDDF8h6MT3w9DKjkxFa903Mt',
       'http://www.moserbaerprojects.com',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1352192814.76/view',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1321971513.67/view',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1333450990.34/view',
       'https://cdm.unfccc.int/Projects/DB/KBS_Cert1356901324.44/view',
       'http://www.cloversolar.com',
       'https://cdm.unfccc.int/filestorage/M/7/A/M7ACU21YQXKW5HF8R03TPIJZENO4B9/PDD_30MW_Gujarat_MSPL%20Limited_rev.pdf?t=NlZ8b2pkZ2FofDBI3_hXb8zCdgFEZVKDwM4F',
       'http://cescnewinitiatives.com/contents/view/DHARIWALINFRASTRUCTURELIMITED/T',
       'http://sanvijay.com/gil.html', 'http://www.rcfltd.com/',
       'http://www.nocil.com/',
       'http://documents.worldbank.org/curated/en/442061468041961880/pdf/multi-page.pdf',
       'https://cdm.unfccc.int/Projects/DB/BVQI1331114727.08/view',
       'https://cdm.unfccc.int/Projects/DB/BVQI1336676184.92/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1354626555.71/view',
       'https://cdm.unfccc.int/Projects/DB/RINA1356126148.87/view',
       'https://cdm.unfccc.int/filestorage/2/F/R/2FRTLO45MK87SJZUYCDN9GWHBAEP3X/7647_PDD_-Ver_12%28PRC%29_Clean.pdf?t=MWx8b2picXlqfDDrpXwmh5zMU8K3xoiuspy8',
       'http://www.tatabpsolar.com',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1352368180.64/view',
       'https://cdm.unfccc.int/filestorage/i/o/Z9N7VXYJ5LIE8WRQM1SB3H4FDA6U2K.pdf/PDD%20with%20revised%20MP%20(ver%2010.0)(Clean%20mode).pdf?t=Qmt8b2picnBrfDBPz_lFTjdrigpAyplKqoQb',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1374760004.52/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1351859167.83/view',
       'https://cdm.unfccc.int/Projects/DB/PJR%20CDM1355461708.47/view',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1353506542.98/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1351718801.6/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1174563974.61/view',
       'https://www.vardhman.com/', 'https://www.kesocorp.com/',
       'http://www.nationalfertilizers.com/',
       'http://www.adityabirla.com/about/Grey-cement',
       'https://cdm.unfccc.int/Projects/DB/SGS-UKL1225104443.35/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1350385874.53/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1172467864.06/view',
       'http://www.gipcl.com',
       'http://www.gmrgroup.in/energy-gmr-gujarat-solar-power-ltd.aspx',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1323254431.52/view',
       'https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1356711565.56/view',
       'https://cdm.unfccc.int/Projects/DB/DNV-CUK1354785555.66/view',
       'https://cdm.unfccc.int/Projects/DB/TUEV-RHEIN1351077305.18/view',
       'https://cdm.unfccc.int/Projects/DB/RWTUV1345031355.85/view'],
      dtype=object)


#For a model, the URL characteristic is irrelevant. We can drop this now.


df['geolocation_source'].unique()

array(['National Renewable Energy Laboratory', 'WRI', nan,
       'Industry About'], dtype=object)


df.drop(['gppd_idnr','url'   ], axis=1,inplace=True)


df.shape

(907, 15)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 907 entries, 0 to 906
Data columns (total 15 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   capacity_mw             907 non-null    float64
 1   latitude                861 non-null    float64
 2   longitude               861 non-null    float64
 3   primary_fuel            907 non-null    object 
 4   commissioning_year      527 non-null    float64
 5   owner                   342 non-null    object 
 6   source                  907 non-null    object 
 7   geolocation_source      888 non-null    object 
 8   year_of_capacity_data   519 non-null    float64
 9   generation_gwh_2014     398 non-null    float64
 10  generation_gwh_2015     422 non-null    float64
 11  generation_gwh_2016     434 non-null    float64
 12  generation_gwh_2017     440 non-null    float64
 13  generation_gwh_2018     448 non-null    float64
 14  generation_data_source  449 non-null    object 
dtypes: float64(10), object(5)
memory usage: 106.4+ KB


df.head(15)


df['owner'].nunique()

280


df['owner'].unique()

array(['Solar Paces', 'Ultratech Cement ltd', 'AES', nan,
       'Hindustan Pvt lt', 'Dr Ssk ltd', 'Ashok Ssk ltd',
       'Shree Sugars ltd', 'Ener-t International Ltd.',
       'Nav Bharat Buildcon Private Limited', 'MEIL Green Power Limited',
       'Madras Cement Limited',
       'DDE Renewable Energy Private Limited (DREPL)',
       'Electromech Maritech Private Limited',
       'Finehope Allied Engineering Private Limited',
       'Khaya Solar Projects Private Limited',
       'Newton Solar Private Limited', 'Saidham Overseas Private Limited',
       'Vasavi Solar Power Private Limited', 'Indian Power ltd',
       'Ballarpur Industries ltd', 'Jk Cement ltd', 'India Ltd icl)',
       'Bharat Petroleum Corporation Limited', 'Bhilwara Energy Limited',
       'Ideal Projects ltd', 'Ugar Works ltd', 'Vishwanath Sugars ltd',
       'Hothur Pvt ltd', 'Janki Corp ltd', 'Mysore Mills ltd',
       'Sunflag Co ltd', 'Varam Pvt ltd', 'Sangam Spinners ltd',
       'Bharat Refinery ltd', 'Binani Industries ltd',
       'Indo Synthetics ltd', 'Dr Babasaheb Ambedkar SSK Limited',
       'AEW Infratech Private Limited', 'Enercon India Ltd.',
       'Green Infra Solar Farms Private Limited (GIS)',
       'OPG Energy Private Limited', 'Punj Lloyd Solar Private Limited',
       'Hiraco Renewable Energy Private Limited (HREPL)',
       'Moser Baer Energy & Development Limited (MBEDL)',
       'Giriraj Enterprises', 'LEPL Projects Limited',
       'Lanco Solar Energy Private Limited',
       'Abellon CleanEnergy Limited', 'Adani Power Limited',
       'Madhav Solar Private Limited', 'Rays Power Private Limited',
       'Acc Acc ltd', 'Birla Corp ltd', 'Hindustan Zinc ltd',
       'Gupta Ltd gepl)', 'Manikghar Cement co', 'Core Fuels ltd',
       'Maharashtra State Power Generation Co Ltd (MAHAGENCO)',
       'Charanka Solar Park', 'India Oil Corporation Limited',
       'Alex Green Energy Private Limited', 'Reliance Power ltd',
       'Bmm Pvt ltd', 'Daund Sugar ltd', 'Davangere Co ltd',
       'Mukand Mukand ltd', 'Shri Ssk ltd', 'Jsw Steel ltd',
       'Shamanur Sugars ltd', 'Jai Industries ltd', 'Government of India',
       'West Coast Paper Mills Ltd.', 'Sanjeev Prakashan',
       'Ujaas Energy Limited', 'Sand Land Real Estates Pvt Ltd',
       'Aravali Infrapower Private Limited (AIPL)', 'Reliance Power',
       'Lanco Infratech', 'Sovox Renewables Private Limited',
       'EMCO Limited', 'Frost International Limited',
       'Gujurat Alkalies and Chemicals Limited', 'Chambal & chem', 'GFL',
       'Lloyds Industries ltd',
       'Bangalore Electricity Supply Company Limited',
       'Grace Industries ltd', 'Orient Cement ltd',
       'SunBorne Energy Gujarat',
       'Pandit Deendayal Petrolium University (PDPU)',
       'National Thermal Power Corporation (NTPC) Limited',
       'Godwari Green Energy Limited',
       'Zamil New Delhi Infrastructure Private Limited',
       'Shree Saibaba Green Power Private Limited', 'Solaer',
       'Cargo Solar Power', 'Amrit Energy Private Limited',
       'Solar Energy Centre', 'Jawahar Ssk ltd', 'Purna Ssk ltd',
       'Rk Pvt ltd', 'Hindustan Zinc Ltd.', 'HZL',
       'Essel MP Energy Limited', 'IL&FS Wind Power Limited',
       'Fortune Five Hydel Projects Limited', 'Harsha Engineers Limited',
       'Karnataka Power Corporation Limited', 'Godawari Energy ltd',
       'Jaypee Ltd jccl)', 'Sunkon Energy Private Limited',
       'West Bengal Energy Development Corporation Limited (WBEDCL)',
       'Energy Infratech Private Limited', 'K. N. Ram',
       'Chettinad Corp ltd', 'Kjs Ahluwalia group', 'Ym Ssk ltd',
       'Maral Overseas ltd', 'Gm Energy ltd', 'Uttam Steels ltd',
       'Sri Sugars ltd', 'Hemarus Technologies ltd', 'Shri Vedganga ssk',
       'Kumbhi Ssk ltd', 'Bannari Sugars ltd', 'Nsl Sugars ltd',
       'Hare Pvt ltd', 'Dcm & chem', 'KS Oils', 'Gem Sugars ltd',
       'KVK Energy Ventures Ltd', 'Bindu Vau Urja Private Limited',
       'SunBorne Energy Gujarat One Pvt', 'Karur Textile Park limited',
       'Azure Power (Rajasthan) Private Limited',
       'Citra Real Estate Limited', 'Sepset Construction Limited',
       'Gujarat Urja Vikas Nigam Limited',
       'Firestone Trading Private Limited',
       'Northern Solaire Prakash Private Limited',
       'Suryauday Solaire Prakash Private Limited', 'ACME Solar Energy',
       'Unity Power Limited', 'Welspun Urja India Limited',
       'Conflux Infratech Private Limited', 'REI Agro Limited',
       'Kohinoor Planet Construction Pvt. Ltd',
       'Tata Power Solar Systems Limited (TPREL)',
       'Loyal Textile Mills Limited', 'LNJ Power Ventures Limited',
       'Tata Co ltd', 'Lokmangal Lokmangal group',
       'Atma Powers Private Limited', 'Essel Clean Energy Limited',
       'IK Energy Private Limited', 'Serum Institute of India Limited',
       'Lanco Solar Power Limited', 'Liberty Oil Mills Limited',
       'Mahatma Power ltd', 'Bharat Corp ltd', 'Maihar Cement ltd',
       'Mangalore & petrochem', 'Manglam Cement ltd',
       'Manikgarh Manikgarh cement', 'Ambuja Cements ltd',
       'Rswm Rswm ltd', 'Jodhpur Vidyut Vitran Nigam Limited', 'MSPL',
       'Greta Energy ltd', 'Ongc Gas corp',
       'Technocraft Technocraft group',
       'Megha Engineering and Infrastructure',
       'Alianz Eco Power Private Limited', 'Nexgen Solux Private Limited',
       'Solairedirect Projects India Private Limited',
       'Grasim Industries ltd', 'Reliance Reliance petrochemicals',
       'Gmt Pvt ltd', 'Purti Sugar ltd', 'Topworth Metals ltd',
       'Orient Power ltd', 'Rattanindia Power ltd', 'National And paper',
       'Nitin Spinners ltd', 'NMDC', 'Nuziveedu Seeds limited',
       'EMC Limited', 'Tadas Wind Energy', 'Bhoruka Power',
       'Godawari Green Energy Limited (GGEL)',
       'Precision Technik Private Limited',
       'Alex Spectrum Radiations Private Limited', 'NuPower',
       'Gangakhed Energy ltd', 'Reliance Ltd ril)', 'Sinarmas Paper ltd',
       'Gujarat Mineral Development Corporation Limited (GMDC)',
       'Tratnagiri Wind Power Projects Private Limited',
       'Patnaik Minerals Pvt. Ltd', 'Vivek Pharmachem (India) Limited',
       'Rajasthan Renewable Energy Corporation Limited (RREC)',
       'Reliance Power Limited', 'Powerica Limited',
       'Integrated Coal Mining (ICML)',
       'Precious Energy Services Pvt Ltd', 'Azure Urja Private Limited',
       'Surana Industries ltd', 'Grasim Cement ltd', 'Spr Pvt ltd',
       'Natural Alliend indust', 'Shree Cement ltd',
       'Rajasthan State Mines and Minerals Limited',
       'National Thermal Power Corporation (NTPC)',
       'Astonfield Management Limited',
       'Welspun Solar Punjab Private Limited',
       'Taxus Infrastructure and Power Projects Pvt Ltd',
       'Ganges Enterprises Private Limited',
       'Ratedi Wind Power Private Limited',
       'Indian Oil Corporation (IOC)',
       'Northwest Energy Private Limited (NWEPL)',
       'SEI Solar Energy Private Limited',
       'Welspun Solar AP Private Limited', 'Relaxo Footwears Limited',
       "Roaring 40's Wind Farms Private Limited",
       'Rucha Soya Industries Limited', 'Sahakar Shiromani vasantrao',
       'Godavari Mills ltd', 'Kranti Ssk ltd', 'Sinewave Pvt ltd',
       'Vasantdada Ssk ltd', 'Shri Hiranyakeshi ssk',
       'Sahakarmaharshi Bhausaheb thor', 'Datta Ssk ltd',
       'Shri Malaprabha ssk', 'Dcm Industries ltd', 'Mula Ssk ltd',
       'SWPPL', 'Aftaab Solar Private Limited (ASPL)',
       'Raajratna Energy Holdings Private Limited (REHPL)',
       'Sai Sulphonates', 'Omega Solar Projects Private Limited',
       'Mono Steel (India) Ltd', 'Jain Solar',
       'Backbone Enterprises Limited',
       'S J Green Park Energy Private Limited',
       'Konark Gujarat Private Limited', 'Shree Naman Developers Ltd.',
       'Moser Baer Clean Energy Limited (MBCEL)',
       'Green Infra Wind Power Projects Limited',
       'Solitaire Energies Pvt Ltd', 'Centaur Mercantile Pvt. Ltd.',
       'Sterling Agro Industries ltd.', 'Sumilon Industries Limited',
       'Clover Solar Private Limited (CSPL)', 'Dhariwal Pvt ltd',
       'Mukesh Gupta group', 'Rashtriya & fert', 'Nocil Nocil rubber',
       'TVS Energy limited', 'TVS Energy Limited',
       'Tadas Wind Energy Private Limited', 'Tata BP Solar India Limited',
       'Tata Power', 'Rayala Wind Power Company Private Limited',
       'Moser Baer Solar Limited (MBSL)', 'Torrent Power Limited', 'UPL',
       'Vardham Vardham group', 'Kesoram Industries ltd',
       'National Ltd nfl)', 'Vikram Vikram cement', 'Vaayu Renew',
       'Gujarat Industries Power Company Limited (GIPCL)',
       'ACME Cleantech Solutions Private Limited',
       'Videocon Industries Limited', 'Weizzmann Forex Limited',
       'Riddhi Siddhi Gluco Biols Limited',
       'Mytrah Energy (India) Limited', 'Yashwantrao Krishna ssk',
       'iEnergy Wind Farms'], dtype=object)


df.groupby('owner')['capacity_mw'].mean()

owner
ACME Cleantech Solutions Private Limited          15.0
ACME Solar Energy                                 25.0
AES                                               39.2
AEW Infratech Private Limited                      1.0
Abellon CleanEnergy Limited                        3.0
                                                  ... 
West Coast Paper Mills Ltd.                       20.0
Yashwantrao Krishna ssk                           16.0
Ym Ssk ltd                                        16.0
Zamil New Delhi Infrastructure Private Limited     1.0
iEnergy Wind Farms                                16.5
Name: capacity_mw, Length: 280, dtype: float64


df['owner'].value_counts(dropna=False)

NaN                                                            565
Jk Cement ltd                                                    4
Acc Acc ltd                                                      4
Sterling Agro Industries ltd.                                    4
Maharashtra State Power Generation Co Ltd (MAHAGENCO)            3
                                                              ... 
Sunkon Energy Private Limited                                    1
West Bengal Energy Development Corporation Limited (WBEDCL)      1
Energy Infratech Private Limited                                 1
K. N. Ram                                                        1
iEnergy Wind Farms                                               1
Name: owner, Length: 281, dtype: int64


df['source'].nunique()

191


df['geolocation_source'].value_counts(dropna=False)

WRI                                     765
Industry About                          119
NaN                                      19
National Renewable Energy Laboratory      4
Name: geolocation_source, dtype: int64


df['generation_data_source'].value_counts(dropna=False)

NaN                              458
Central Electricity Authority    449
Name: generation_data_source, dtype: int64


df.drop(['owner','source','generation_data_source','geolocation_source'], axis=1,inplace=True)


df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 907 entries, 0 to 906
Data columns (total 11 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   capacity_mw            907 non-null    float64
 1   latitude               861 non-null    float64
 2   longitude              861 non-null    float64
 3   primary_fuel           907 non-null    object 
 4   commissioning_year     527 non-null    float64
 5   year_of_capacity_data  519 non-null    float64
 6   generation_gwh_2014    398 non-null    float64
 7   generation_gwh_2015    422 non-null    float64
 8   generation_gwh_2016    434 non-null    float64
 9   generation_gwh_2017    440 non-null    float64
 10  generation_gwh_2018    448 non-null    float64
dtypes: float64(10), object(1)
memory usage: 78.1+ KB


df.isnull().sum()

capacity_mw                0
latitude                  46
longitude                 46
primary_fuel               0
commissioning_year       380
year_of_capacity_data    388
generation_gwh_2014      509
generation_gwh_2015      485
generation_gwh_2016      473
generation_gwh_2017      467
generation_gwh_2018      459
dtype: int64


#% of data missing

round((df.isnull().sum()/df.shape[0])*100,2)

capacity_mw               0.00
latitude                  5.07
longitude                 5.07
primary_fuel              0.00
commissioning_year       41.90
year_of_capacity_data    42.78
generation_gwh_2014      56.12
generation_gwh_2015      53.47
generation_gwh_2016      52.15
generation_gwh_2017      51.49
generation_gwh_2018      50.61
dtype: float64


sns.countplot(df['primary_fuel'],order=df['primary_fuel'].value_counts().index)

<AxesSubplot:xlabel='primary_fuel', ylabel='count'>


df['primary_fuel'].value_counts(normalize=True,dropna=False)

Coal       0.284454
Hydro      0.276736
Solar      0.140022
Wind       0.135612
Gas        0.076075
Biomass    0.055127
Oil        0.022051
Nuclear    0.009923
Name: primary_fuel, dtype: float64


df['primary_fuel'].value_counts()

Coal       258
Hydro      251
Solar      127
Wind       123
Gas         69
Biomass     50
Oil         20
Nuclear      9
Name: primary_fuel, dtype: int64


sns.distplot(df['capacity_mw'])

<AxesSubplot:xlabel='capacity_mw', ylabel='Density'>


df['capacity_mw'].describe().T

count     907.000000
mean      326.223755
std       590.085456
min         0.000000
25%        16.725000
50%        59.200000
75%       385.250000
max      4760.000000
Name: capacity_mw, dtype: float64


#This distribution is not normal.


sns.countplot(df['commissioning_year'])

<AxesSubplot:xlabel='commissioning_year', ylabel='count'>


df['commissioning_year'].value_counts(dropna=False)

NaN       380
2015.0     28
2013.0     25
2012.0     23
2016.0     19
         ... 
1946.0      1
1956.0      1
1937.0      1
1949.0      1
1953.0      1
Name: commissioning_year, Length: 74, dtype: int64


df.head(2)


sns.distplot(df['year_of_capacity_data'])

<AxesSubplot:xlabel='year_of_capacity_data', ylabel='Density'>


df['year_of_capacity_data'].unique()

array([  nan, 2019.])


df['year_of_capacity_data'].value_counts(dropna=False)

2019.0    519
NaN       388
Name: year_of_capacity_data, dtype: int64


df.drop(['year_of_capacity_data'],axis=1,inplace=True)


df.head(2)


sns.distplot(df['generation_gwh_2014'])

<AxesSubplot:xlabel='generation_gwh_2014', ylabel='Density'>


sns.distplot(df['generation_gwh_2015'])

<AxesSubplot:xlabel='generation_gwh_2015', ylabel='Density'>


sns.distplot(df['generation_gwh_2016'])

<AxesSubplot:xlabel='generation_gwh_2016', ylabel='Density'>


sns.distplot(df['generation_gwh_2017'])

<AxesSubplot:xlabel='generation_gwh_2017', ylabel='Density'>


sns.distplot(df['generation_gwh_2018'])

<AxesSubplot:xlabel='generation_gwh_2018', ylabel='Density'>


df.head(2)


sns.countplot(df['primary_fuel'],hue=df['capacity_mw'])

<AxesSubplot:xlabel='primary_fuel', ylabel='count'>


visual=df.groupby('primary_fuel')['capacity_mw'].mean().reset_index()
visual


df['primary_fuel'].value_counts(normalize=True,dropna=False)

Coal       0.284454
Hydro      0.276736
Solar      0.140022
Wind       0.135612
Gas        0.076075
Biomass    0.055127
Oil        0.022051
Nuclear    0.009923
Name: primary_fuel, dtype: float64


# primary_fuel and Capacity_mw
visual


sns.barplot(visual['primary_fuel'], visual['capacity_mw'])

<AxesSubplot:xlabel='primary_fuel', ylabel='capacity_mw'>


visual.sort_values('capacity_mw',ascending=False)


df.groupby('primary_fuel')['commissioning_year'].count()

primary_fuel
Biomass      0
Coal       189
Gas         59
Hydro      251
Nuclear      8
Oil         12
Solar        8
Wind         0
Name: commissioning_year, dtype: int64


df['commissioning_year'].nunique()

73


df['commissioning_year'].unique()

array([2011.,   nan, 2004., 2015., 2005., 1970., 2010., 2008., 2014.,
       1985., 1986., 1987., 2000., 1989., 2017., 1977., 1980., 2003.,
       1984., 1976., 1996., 1991., 2002., 2001., 2006., 2007., 1988.,
       1973., 2012., 1968., 1964., 2016., 2013., 1962., 1998., 1993.,
       1997., 1999., 1994., 1975., 1992., 1972., 1979., 1967., 1995.,
       1983., 1981., 2018., 1959., 1961., 1957., 1978., 1963., 1971.,
       1955., 2009., 1966., 1965., 1960., 1949., 1952., 1990., 1982.,
       1937., 1956., 1946., 1958., 1943., 1954., 1969., 1939., 1927.,
       1974., 1953.])


df['commissioning_year'].value_counts()

2015.0    28
2013.0    25
2012.0    23
2016.0    19
2010.0    18
          ..
1946.0     1
1956.0     1
1937.0     1
1949.0     1
1953.0     1
Name: commissioning_year, Length: 73, dtype: int64


df.head(5)


plt.figure(figsize=(12,8))
sns.scatterplot(x='generation_gwh_2014',y='capacity_mw',hue='primary_fuel', data=df)

<AxesSubplot:xlabel='generation_gwh_2014', ylabel='capacity_mw'>


df.groupby('primary_fuel')['generation_gwh_2014'].median()

primary_fuel
Biomass            NaN
Coal       3193.101000
Gas         559.010000
Hydro       386.005275
Nuclear    3875.323150
Oil           0.000000
Solar              NaN
Wind               NaN
Name: generation_gwh_2014, dtype: float64


plt.figure(figsize=(12,8))
sns.scatterplot(x='generation_gwh_2015',y='capacity_mw',hue='primary_fuel', data=df)

<AxesSubplot:xlabel='generation_gwh_2015', ylabel='capacity_mw'>


df.groupby('primary_fuel')['generation_gwh_2015'].mean()

primary_fuel
Biomass            NaN
Coal       4649.585751
Gas         840.159648
Hydro       709.991238
Nuclear    4234.875000
Oil          21.710086
Solar              NaN
Wind               NaN
Name: generation_gwh_2015, dtype: float64


df.groupby('primary_fuel')['generation_gwh_2015']

<pandas.core.groupby.generic.SeriesGroupBy object at 0x00000179AA43AB20>


df[df['primary_fuel']=='Biomass']


plt.figure(figsize=(12,8))
sns.scatterplot(x='generation_gwh_2016',y='capacity_mw',hue='primary_fuel', data=df)

<AxesSubplot:xlabel='generation_gwh_2016', ylabel='capacity_mw'>


plt.figure(figsize=(12,8))
sns.scatterplot(x='generation_gwh_2017',y='capacity_mw',hue='primary_fuel', data=df)

<AxesSubplot:xlabel='generation_gwh_2017', ylabel='capacity_mw'>


plt.figure(figsize=(12,8))
sns.scatterplot(x='generation_gwh_2018',y='capacity_mw',hue='primary_fuel', data=df)

<AxesSubplot:xlabel='generation_gwh_2018', ylabel='capacity_mw'>


sns.scatterplot(x='capacity_mw',y='commissioning_year',hue='primary_fuel', data=df)

<AxesSubplot:xlabel='capacity_mw', ylabel='commissioning_year'>


df.corr()


plt.figure(figsize=(12,8))
sns.heatmap(df.corr(),annot=True)

<AxesSubplot:>


plt.figure(figsize=(12,8))
sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x179aa1bf280>

<Figure size 1200x800 with 0 Axes>


df.groupby('primary_fuel')['generation_gwh_2014'].mean()

primary_fuel
Biomass            NaN
Coal       4737.279310
Gas         756.601441
Hydro       780.429852
Nuclear    4867.556164
Oil         130.881366
Solar              NaN
Wind               NaN
Name: generation_gwh_2014, dtype: float64


df.groupby('primary_fuel')['generation_gwh_2015'].mean()

primary_fuel
Biomass            NaN
Coal       4649.585751
Gas         840.159648
Hydro       709.991238
Nuclear    4234.875000
Oil          21.710086
Solar              NaN
Wind               NaN
Name: generation_gwh_2015, dtype: float64


df.groupby('primary_fuel')['generation_gwh_2016'].mean()

primary_fuel
Biomass            NaN
Coal       4761.233946
Gas         826.305503
Hydro       699.318079
Nuclear    4272.608750
Oil           4.796871
Solar              NaN
Wind               NaN
Name: generation_gwh_2016, dtype: float64


df.groupby('primary_fuel')['generation_gwh_2017'].mean()

primary_fuel
Biomass            NaN
Coal       4870.537285
Gas         881.216187
Hydro       693.884741
Nuclear    4323.415000
Oil           0.167471
Solar              NaN
Wind               NaN
Name: generation_gwh_2017, dtype: float64


df.groupby('primary_fuel')['generation_gwh_2018'].mean()

primary_fuel
Biomass            NaN
Coal       5036.420635
Gas         846.658418
Hydro       737.636455
Nuclear    4277.031250
Oil           0.295215
Solar              NaN
Wind               NaN
Name: generation_gwh_2018, dtype: float64


the_mean = df.groupby('primary_fuel').mean()
the_mean


df.plot(x="longitude", y="latitude", kind="scatter",colormap="YlOrRd")

<AxesSubplot:xlabel='longitude', ylabel='latitude'>


visual


data=temp[['longitude','latitude','name']].copy()


data.dropna(inplace=True)


data.isnull().sum()

longitude    0
latitude     0
name         0
dtype: int64


pip install folium

Collecting folium
  Downloading folium-0.14.0-py2.py3-none-any.whl (102 kB)
     -------------------------------------- 102.3/102.3 kB 1.5 MB/s eta 0:00:00
Requirement already satisfied: requests in c:\users\lenovo\anaconda3\lib\site-packages (from folium) (2.28.1)
Requirement already satisfied: jinja2>=2.9 in c:\users\lenovo\anaconda3\lib\site-packages (from folium) (2.11.3)
Requirement already satisfied: numpy in c:\users\lenovo\anaconda3\lib\site-packages (from folium) (1.21.5)
Collecting branca>=0.6.0
  Downloading branca-0.6.0-py3-none-any.whl (24 kB)
Requirement already satisfied: MarkupSafe>=0.23 in c:\users\lenovo\anaconda3\lib\site-packages (from jinja2>=2.9->folium) (2.0.1)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\lenovo\anaconda3\lib\site-packages (from requests->folium) (2022.9.14)
Requirement already satisfied: charset-normalizer<3,>=2 in c:\users\lenovo\anaconda3\lib\site-packages (from requests->folium) (2.0.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\lenovo\anaconda3\lib\site-packages (from requests->folium) (1.26.11)
Requirement already satisfied: idna<4,>=2.5 in c:\users\lenovo\anaconda3\lib\site-packages (from requests->folium) (3.3)
Installing collected packages: branca, folium
Successfully installed branca-0.6.0 folium-0.14.0
Note: you may need to restart the kernel to use updated packages.


#installing and importing folium
import folium


#The first step is creating a map of the place we want to go.Passing the mean of the latitude and longitude coordinates
#we need to center the map there using the location parameter.

map= folium.Map(location=[data['latitude'].mean(),data['longitude'].mean()],zoom_start=5,control_scale=True)


# Adding markers to the map

for index,location in data.iterrows():
    folium.Marker([location['latitude'],location['longitude']],popup=location['name']).add_to(map)

map


df.head()


the_mean

df


df['latitude'].mode()

0    19.0004
1    24.1917
Name: latitude, dtype: float64


df['longitude'].mode()

0    71.6917
Name: longitude, dtype: float64


#Filling in the null values by mode since we can't identify a geolocation relationship with any other column.

df[['latitude','longitude']].mode()


df['commissioning_year'].mode()

0    2015.0
Name: commissioning_year, dtype: float64


# Substituting 19.0004 for latitude and 71.6917 for longitude.


df['latitude'].fillna(df['latitude'].mode()[0], inplace=True)
df['longitude'].fillna(df['longitude'].mode()[0], inplace=True)
df['commissioning_year'].fillna(df['commissioning_year'].mode()[0], inplace=True)


df['latitude'].isnull().sum()

0


df['longitude'].isnull().sum()

0


df['commissioning_year'].isnull().sum()

0


sns.boxplot(df['generation_gwh_2014'])

<AxesSubplot:xlabel='generation_gwh_2014'>


# Determining the median power produced in 2014 according to the fuel type.

df.groupby('primary_fuel').mean()


df.head()


df['primary_fuel']

0      Solar
1       Coal
2       Wind
3        Gas
4       Coal
       ...  
902     Coal
903    Solar
904     Wind
905     Coal
906     Wind
Name: primary_fuel, Length: 907, dtype: object


X= df.drop('primary_fuel', axis=1)
Y= df['primary_fuel']


X.shape , Y.shape

((907, 9), (907,))


from sklearn.preprocessing import LabelEncoder
le= LabelEncoder()


X['primary_fuel']=le.fit_transform(Y)


plt.figure(figsize=(12,8))
sns.heatmap(X.corr(),annot=True)

<AxesSubplot:>


#Low correlation exists between the target variable and mainly independent factors.
#generation columns and the target variable have a strong negative correlation.
#Despite the fact that there is some multicolinearity among the independent variables


df.groupby('primary_fuel')['generation_gwh_2014'].mean()

primary_fuel
Biomass            NaN
Coal       4737.279310
Gas         756.601441
Hydro       780.429852
Nuclear    4867.556164
Oil         130.881366
Solar              NaN
Wind               NaN
Name: generation_gwh_2014, dtype: float64


df.head()


df[df['primary_fuel']=='Solar'].index

Int64Index([  0,  25,  26,  28,  32,  33,  34,  35,  36,  37,
            ...
            771, 779, 827, 829, 832, 835, 887, 894, 895, 903],
           dtype='int64', length=127)


df.drop(df[df['primary_fuel']=='Solar'].index, inplace = True)
df.drop(df[df['primary_fuel']=='Wind'].index, inplace = True)
df.drop(df[df['primary_fuel']=='Biomass'].index, inplace = True)


df.shape

(607, 10)


df.isnull().sum()

capacity_mw              0
latitude                 0
longitude                0
primary_fuel             0
commissioning_year       0
generation_gwh_2014    209
generation_gwh_2015    185
generation_gwh_2016    173
generation_gwh_2017    167
generation_gwh_2018    159
dtype: int64


# Will now fill in the gaps in the generation column group using the average fuel type.


df.groupby('primary_fuel')['generation_gwh_2014'].mean()

primary_fuel
Coal       4737.279310
Gas         756.601441
Hydro       780.429852
Nuclear    4867.556164
Oil         130.881366
Name: generation_gwh_2014, dtype: float64


df['generation_gwh_2014']=np.where(df['primary_fuel']=='Coal',df['generation_gwh_2014'].fillna(4737.279310),df['generation_gwh_2014']  )
df['generation_gwh_2014']=np.where(df['primary_fuel']=='Gas',df['generation_gwh_2014'].fillna(7756.601441),df['generation_gwh_2014']  )
df['generation_gwh_2014']=np.where(df['primary_fuel']=='Hydro',df['generation_gwh_2014'].fillna(780.429852),df['generation_gwh_2014']  )
df['generation_gwh_2014']=np.where(df['primary_fuel']=='Nuclear',df['generation_gwh_2014'].fillna(4867.556164),df['generation_gwh_2014']  )
df['generation_gwh_2014']=np.where(df['primary_fuel']=='Oil',df['generation_gwh_2014'].fillna(130.881366),df['generation_gwh_2014']  )


df.groupby('primary_fuel')['generation_gwh_2015'].mean()

primary_fuel
Coal       4649.585751
Gas         840.159648
Hydro       709.991238
Nuclear    4234.875000
Oil          21.710086
Name: generation_gwh_2015, dtype: float64


df['generation_gwh_2015']=np.where(df['primary_fuel']=='Coal',df['generation_gwh_2015'].fillna(4649.585751),df['generation_gwh_2015']  )
df['generation_gwh_2015']=np.where(df['primary_fuel']=='Gas',df['generation_gwh_2015'].fillna(840.159648),df['generation_gwh_2015']  )
df['generation_gwh_2015']=np.where(df['primary_fuel']=='Hydro',df['generation_gwh_2015'].fillna(709.991238),df['generation_gwh_2015']  )
df['generation_gwh_2015']=np.where(df['primary_fuel']=='Nuclear',df['generation_gwh_2015'].fillna(4234.875000),df['generation_gwh_2015']  )
df['generation_gwh_2015']=np.where(df['primary_fuel']=='Oil',df['generation_gwh_2015'].fillna(21.710086),df['generation_gwh_2015'] )


df.groupby('primary_fuel')['generation_gwh_2016'].mean()

primary_fuel
Coal       4761.233946
Gas         826.305503
Hydro       699.318079
Nuclear    4272.608750
Oil           4.796871
Name: generation_gwh_2016, dtype: float64


df['generation_gwh_2016']=np.where(df['primary_fuel']=='Coal',df['generation_gwh_2016'].fillna(4761.233946),df['generation_gwh_2016']  )
df['generation_gwh_2016']=np.where(df['primary_fuel']=='Gas',df['generation_gwh_2016'].fillna(826.305503),df['generation_gwh_2016']  )
df['generation_gwh_2016']=np.where(df['primary_fuel']=='Hydro',df['generation_gwh_2016'].fillna(699.318079),df['generation_gwh_2016']  )
df['generation_gwh_2016']=np.where(df['primary_fuel']=='Nuclear',df['generation_gwh_2016'].fillna(4272.608750),df['generation_gwh_2016']  )
df['generation_gwh_2016']=np.where(df['primary_fuel']=='Oil',df['generation_gwh_2016'].fillna(4.796871),df['generation_gwh_2016']  )


df.groupby('primary_fuel')['generation_gwh_2017'].mean()

primary_fuel
Coal       4870.537285
Gas         881.216187
Hydro       693.884741
Nuclear    4323.415000
Oil           0.167471
Name: generation_gwh_2017, dtype: float64


df['generation_gwh_2017']=np.where(df['primary_fuel']=='Coal',df['generation_gwh_2017'].fillna(4870.537285),df['generation_gwh_2017']  )
df['generation_gwh_2017']=np.where(df['primary_fuel']=='Gas',df['generation_gwh_2017'].fillna(881.216187),df['generation_gwh_2017']  )
df['generation_gwh_2017']=np.where(df['primary_fuel']=='Hydro',df['generation_gwh_2017'].fillna(693.884741),df['generation_gwh_2017']  )
df['generation_gwh_2017']=np.where(df['primary_fuel']=='Nuclear',df['generation_gwh_2017'].fillna(4323.415000),df['generation_gwh_2017']  )
df['generation_gwh_2017']=np.where(df['primary_fuel']=='Oil',df['generation_gwh_2017'].fillna(0.167471),df['generation_gwh_2017'] )


df.groupby('primary_fuel')['generation_gwh_2018'].mean()

primary_fuel
Coal       5036.420635
Gas         846.658418
Hydro       737.636455
Nuclear    4277.031250
Oil           0.295215
Name: generation_gwh_2018, dtype: float64


df['generation_gwh_2018']=np.where(df['primary_fuel']=='Coal',df['generation_gwh_2018'].fillna(5036.420635),df['generation_gwh_2018']  )
df['generation_gwh_2018']=np.where(df['primary_fuel']=='Gas',df['generation_gwh_2018'].fillna(846.658418),df['generation_gwh_2018']  )
df['generation_gwh_2018']=np.where(df['primary_fuel']=='Hydro',df['generation_gwh_2018'].fillna(737.636455),df['generation_gwh_2018']  )
df['generation_gwh_2018']=np.where(df['primary_fuel']=='Nuclear',df['generation_gwh_2018'].fillna(4277.031250),df['generation_gwh_2018']  )
df['generation_gwh_2018']=np.where(df['primary_fuel']=='Oil',df['generation_gwh_2018'].fillna(0.295215),df['generation_gwh_2018']  )


df.isnull().sum()

capacity_mw            0
latitude               0
longitude              0
primary_fuel           0
commissioning_year     0
generation_gwh_2014    0
generation_gwh_2015    0
generation_gwh_2016    0
generation_gwh_2017    0
generation_gwh_2018    0
dtype: int64


df.head()


X= df.drop('primary_fuel',axis=1)
Y = df['primary_fuel']


X.shape , Y.shape

((607, 9), (607,))


X.head()


X.skew()

capacity_mw            2.523310
latitude              -0.058579
longitude              0.986666
commissioning_year    -1.198052
generation_gwh_2014    3.078832
generation_gwh_2015    3.826520
generation_gwh_2016    3.622211
generation_gwh_2017    3.673231
generation_gwh_2018    3.559440
dtype: float64


for i in X.columns:
    sns.boxplot(X[i])
    plt.show()


#We won't deal with longitude outliers because those are places.
#Data by the commissioning year are categorical.


for i in X.columns:
    sns.distplot(X[i])
    plt.show()


col=['capacity_mw','generation_gwh_2014','generation_gwh_2015','generation_gwh_2016','generation_gwh_2017','generation_gwh_2018']


for i in col:
    IQR= X[i].quantile(.75)-X[i].quantile(.25)
    upper= (X[i].quantile(.75) +(1.5* IQR))
    X[i]=np.where(X[i]>upper,upper,X[i])


for i in X.columns:
    sns.boxplot(X[i])
    plt.show()


X.skew()

capacity_mw            1.109633
latitude              -0.058579
longitude              0.986666
commissioning_year    -1.198052
generation_gwh_2014    1.282673
generation_gwh_2015    1.518384
generation_gwh_2016    1.476022
generation_gwh_2017    1.409096
generation_gwh_2018    1.454508
dtype: float64


# By managing outliers, skewness has been reduced.


v=X.copy()


v['Total']=X['generation_gwh_2014']+X['generation_gwh_2015']+X['generation_gwh_2016']+X['generation_gwh_2017']+X['generation_gwh_2018']


v.drop(['generation_gwh_2014','generation_gwh_2015','generation_gwh_2016','generation_gwh_2017','generation_gwh_2018'],axis=1,inplace=True)

v


from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
scaled=sc.fit_transform(v)
scaled

array([[-0.6584317 ,  0.53608541, -0.77608434,  0.91147876,  0.96271486],
       [-0.58105619,  0.39288959,  2.50590045,  0.26575529, -0.62323304],
       [ 2.17623053,  0.08718866,  0.75215301,  0.91147876,  1.25822839],
       ...,
       [-0.64044712, -1.32776397, -0.1932456 , -0.37996817, -0.90577034],
       [ 2.17623053, -0.81914763, -0.23772065,  0.97018089, -0.39904358],
       [-0.69607384,  0.4694868 , -0.94483485,  0.91147876,  0.96271486]])


from statsmodels.stats.outliers_influence import variance_inflation_factor
VIF= pd.DataFrame()
VIF['features']=v.columns


VIF['vif']= [variance_inflation_factor(scaled,i) for i in range(len(v.columns))]

VIF


# Controlled multicolinearity


from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
Y=le.fit_transform(Y)


from sklearn.preprocessing import power_transform
transformed=power_transform(scaled)


inp=pd.DataFrame(transformed, columns=v.columns)


from imblearn.over_sampling import SMOTE
sm=SMOTE()
X,Y=sm.fit_resample(inp,Y)


X.shape , Y.shape

((1290, 5), (1290,))


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,f1_score


# Best Random_state to find

maxaccu=0
maxRS=0

for i in range(0,200):
    x_train,x_test,y_train,y_test= train_test_split(X,Y,random_state=i,test_size=.20)
    LR= LogisticRegression()
    LR.fit(x_train,y_train)
    pred= LR.predict(x_test)
    acc=accuracy_score(y_test,pred)
    if acc>maxaccu:
        maxaccu=acc
        maxRS=i
print("Best accuracy is ",maxaccu,"on Random State =",maxRS)

Best accuracy is  0.7906976744186046 on Random State = 84


#Random State=84


x_train,x_test,y_train,y_test= train_test_split(X,Y,random_state=84,test_size=.20)


from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import RidgeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier


LR_model= LogisticRegression()
RD_model= RidgeClassifier()
DT_model= DecisionTreeClassifier()
SV_model= SVC()
KNR_model= KNeighborsClassifier()
RFR_model= RandomForestClassifier()
XGB_model= XGBClassifier()
SGH_model= SGDClassifier()
Bag_model=BaggingClassifier()
ADA_model=AdaBoostClassifier()
GB_model= GradientBoostingClassifier()

model=[LR_model,RD_model,DT_model,SV_model,KNR_model,RFR_model,XGB_model,SGH_model,Bag_model,ADA_model,GB_model ]


for m in model:
    m.fit(x_train,y_train)
    m.score(x_train,y_train)
    pred= m.predict(x_test)
    print('Accuracy_Score of ',m, 'is', accuracy_score(y_test,pred)*100)
    print('Confusion Matrix of ',m,' is \n', confusion_matrix(y_test,pred) )
    print(classification_report(y_test,pred))
    print('*'*50)

Accuracy_Score of  LogisticRegression() is 75.5813953488372
Confusion Matrix of  LogisticRegression()  is 
 [[41  4  2 12  0]
 [ 3 25 10  7  4]
 [ 2  5 28  1  0]
 [ 7  0  4 42  0]
 [ 0  0  2  0 59]]
              precision    recall  f1-score   support

           0       0.77      0.69      0.73        59
           1       0.74      0.51      0.60        49
           2       0.61      0.78      0.68        36
           3       0.68      0.79      0.73        53
           4       0.94      0.97      0.95        61

    accuracy                           0.76       258
   macro avg       0.75      0.75      0.74       258
weighted avg       0.76      0.76      0.75       258

**************************************************
Accuracy_Score of  RidgeClassifier() is 66.66666666666666
Confusion Matrix of  RidgeClassifier()  is 
 [[36  4  4 15  0]
 [ 8  4 13  9 15]
 [ 2  1 24  2  7]
 [ 0  0  4 49  0]
 [ 0  0  2  0 59]]
              precision    recall  f1-score   support

           0       0.78      0.61      0.69        59
           1       0.44      0.08      0.14        49
           2       0.51      0.67      0.58        36
           3       0.65      0.92      0.77        53
           4       0.73      0.97      0.83        61

    accuracy                           0.67       258
   macro avg       0.62      0.65      0.60       258
weighted avg       0.64      0.67      0.62       258

**************************************************
Accuracy_Score of  DecisionTreeClassifier() is 87.20930232558139
Confusion Matrix of  DecisionTreeClassifier()  is 
 [[47  4  5  3  0]
 [ 4 37  7  0  1]
 [ 3  2 30  0  1]
 [ 2  0  0 51  0]
 [ 0  0  1  0 60]]
              precision    recall  f1-score   support

           0       0.84      0.80      0.82        59
           1       0.86      0.76      0.80        49
           2       0.70      0.83      0.76        36
           3       0.94      0.96      0.95        53
           4       0.97      0.98      0.98        61

    accuracy                           0.87       258
   macro avg       0.86      0.87      0.86       258
weighted avg       0.88      0.87      0.87       258

**************************************************
Accuracy_Score of  SVC() is 88.75968992248062
Confusion Matrix of  SVC()  is 
 [[46  4  2  7  0]
 [ 1 40  2  3  3]
 [ 2  1 29  1  3]
 [ 0  0  0 53  0]
 [ 0  0  0  0 61]]
              precision    recall  f1-score   support

           0       0.94      0.78      0.85        59
           1       0.89      0.82      0.85        49
           2       0.88      0.81      0.84        36
           3       0.83      1.00      0.91        53
           4       0.91      1.00      0.95        61

    accuracy                           0.89       258
   macro avg       0.89      0.88      0.88       258
weighted avg       0.89      0.89      0.89       258

**************************************************
Accuracy_Score of  KNeighborsClassifier() is 88.75968992248062
Confusion Matrix of  KNeighborsClassifier()  is 
 [[48  1  2  8  0]
 [ 1 40  4  2  2]
 [ 2  2 30  0  2]
 [ 0  1  0 52  0]
 [ 0  1  1  0 59]]
              precision    recall  f1-score   support

           0       0.94      0.81      0.87        59
           1       0.89      0.82      0.85        49
           2       0.81      0.83      0.82        36
           3       0.84      0.98      0.90        53
           4       0.94      0.97      0.95        61

    accuracy                           0.89       258
   macro avg       0.88      0.88      0.88       258
weighted avg       0.89      0.89      0.89       258

**************************************************
Accuracy_Score of  RandomForestClassifier() is 94.18604651162791
Confusion Matrix of  RandomForestClassifier()  is 
 [[53  0  3  3  0]
 [ 0 43  6  0  0]
 [ 2  1 33  0  0]
 [ 0  0  0 53  0]
 [ 0  0  0  0 61]]
              precision    recall  f1-score   support

           0       0.96      0.90      0.93        59
           1       0.98      0.88      0.92        49
           2       0.79      0.92      0.85        36
           3       0.95      1.00      0.97        53
           4       1.00      1.00      1.00        61

    accuracy                           0.94       258
   macro avg       0.93      0.94      0.93       258
weighted avg       0.95      0.94      0.94       258

**************************************************
Accuracy_Score of  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...) is 92.63565891472868
Confusion Matrix of  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...)  is 
 [[51  0  3  5  0]
 [ 0 42  6  0  1]
 [ 4  0 32  0  0]
 [ 0  0  0 53  0]
 [ 0  0  0  0 61]]
              precision    recall  f1-score   support

           0       0.93      0.86      0.89        59
           1       1.00      0.86      0.92        49
           2       0.78      0.89      0.83        36
           3       0.91      1.00      0.95        53
           4       0.98      1.00      0.99        61

    accuracy                           0.93       258
   macro avg       0.92      0.92      0.92       258
weighted avg       0.93      0.93      0.93       258

**************************************************
Accuracy_Score of  SGDClassifier() is 57.36434108527132
Confusion Matrix of  SGDClassifier()  is 
 [[37  8  1 13  0]
 [13 27  5  4  0]
 [ 2  5 25  4  0]
 [26  2  4 21  0]
 [ 0  5 18  0 38]]
              precision    recall  f1-score   support

           0       0.47      0.63      0.54        59
           1       0.57      0.55      0.56        49
           2       0.47      0.69      0.56        36
           3       0.50      0.40      0.44        53
           4       1.00      0.62      0.77        61

    accuracy                           0.57       258
   macro avg       0.60      0.58      0.57       258
weighted avg       0.62      0.57      0.58       258

**************************************************
Accuracy_Score of  BaggingClassifier() is 90.31007751937985
Confusion Matrix of  BaggingClassifier()  is 
 [[50  1  5  3  0]
 [ 5 41  2  0  1]
 [ 2  1 32  0  1]
 [ 4  0  0 49  0]
 [ 0  0  0  0 61]]
              precision    recall  f1-score   support

           0       0.82      0.85      0.83        59
           1       0.95      0.84      0.89        49
           2       0.82      0.89      0.85        36
           3       0.94      0.92      0.93        53
           4       0.97      1.00      0.98        61

    accuracy                           0.90       258
   macro avg       0.90      0.90      0.90       258
weighted avg       0.91      0.90      0.90       258

**************************************************
Accuracy_Score of  AdaBoostClassifier() is 52.71317829457365
Confusion Matrix of  AdaBoostClassifier()  is 
 [[42  8  0  9  0]
 [ 4 31  9  4  1]
 [ 0  7 28  1  0]
 [29  0  2 22  0]
 [ 0  7 41  0 13]]
              precision    recall  f1-score   support

           0       0.56      0.71      0.63        59
           1       0.58      0.63      0.61        49
           2       0.35      0.78      0.48        36
           3       0.61      0.42      0.49        53
           4       0.93      0.21      0.35        61

    accuracy                           0.53       258
   macro avg       0.61      0.55      0.51       258
weighted avg       0.63      0.53      0.51       258

**************************************************
Accuracy_Score of  GradientBoostingClassifier() is 90.31007751937985
Confusion Matrix of  GradientBoostingClassifier()  is 
 [[50  1  4  4  0]
 [ 1 41  6  0  1]
 [ 3  3 29  0  1]
 [ 0  0  0 53  0]
 [ 0  0  1  0 60]]
              precision    recall  f1-score   support

           0       0.93      0.85      0.88        59
           1       0.91      0.84      0.87        49
           2       0.72      0.81      0.76        36
           3       0.93      1.00      0.96        53
           4       0.97      0.98      0.98        61

    accuracy                           0.90       258
   macro avg       0.89      0.89      0.89       258
weighted avg       0.91      0.90      0.90       258

**************************************************


from sklearn.model_selection import cross_val_score


for i in model:
    print('Accuracy_Score of ',i, 'is', accuracy_score(y_test,i.predict(x_test))*100)
    print("cross Validation accuracy score of ",i ," is ",cross_val_score(i,X,Y,cv=5, scoring='accuracy').mean()*100)
    print('*'*50)

Accuracy_Score of  LogisticRegression() is 75.5813953488372
cross Validation accuracy score of  LogisticRegression()  is  72.24806201550386
**************************************************
Accuracy_Score of  RidgeClassifier() is 66.66666666666666
cross Validation accuracy score of  RidgeClassifier()  is  63.100775193798455
**************************************************
Accuracy_Score of  DecisionTreeClassifier() is 87.20930232558139
cross Validation accuracy score of  DecisionTreeClassifier()  is  87.67441860465117
**************************************************
Accuracy_Score of  SVC() is 88.75968992248062
cross Validation accuracy score of  SVC()  is  86.2015503875969
**************************************************
Accuracy_Score of  KNeighborsClassifier() is 88.75968992248062
cross Validation accuracy score of  KNeighborsClassifier()  is  87.90697674418604
**************************************************
Accuracy_Score of  RandomForestClassifier() is 94.18604651162791
cross Validation accuracy score of  RandomForestClassifier()  is  91.86046511627907
**************************************************
Accuracy_Score of  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...) is 92.63565891472868
cross Validation accuracy score of  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...)  is  91.7829457364341
**************************************************
Accuracy_Score of  SGDClassifier() is 57.36434108527132
cross Validation accuracy score of  SGDClassifier()  is  65.42635658914729
**************************************************
Accuracy_Score of  BaggingClassifier() is 90.31007751937985
cross Validation accuracy score of  BaggingClassifier()  is  89.53488372093024
**************************************************
Accuracy_Score of  AdaBoostClassifier() is 52.71317829457365
cross Validation accuracy score of  AdaBoostClassifier()  is  53.4108527131783
**************************************************
Accuracy_Score of  GradientBoostingClassifier() is 90.31007751937985
cross Validation accuracy score of  GradientBoostingClassifier()  is  90.31007751937985
**************************************************


#XGBoost performed a fantastic job with the generalization model.Accuracy_XGBClassifier's rating is 91.86.


from sklearn.model_selection import GridSearchCV, RandomizedSearchCV


params={"learning_rate"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,
         "max_depth"        : [ 3, 4, 5, 6, 8, 10, 12, 15],
         "min_child_weight" : [ 1, 3, 5, 7 ],
         "gamma"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],
         "colsample_bytree" : [ 0.3, 0.4, 0.5 , 0.7 ] }


GCV= RandomizedSearchCV(XGB_model,param_distributions=params,cv=5,scoring='accuracy', n_jobs=-1)
GCV.fit(x_train,y_train)

RandomizedSearchCV(cv=5,
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           callbacks=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None,
                                           early_stopping_rounds=None,
                                           enable_categorical=False,
                                           eval_metric=None, feature_types=None,
                                           gamma=None, gpu_id=None,
                                           grow_policy=None,
                                           importance_type=None,
                                           interaction_constraints=None,
                                           learning_rate...
                                           monotone_constraints=None,
                                           n_estimators=100, n_jobs=None,
                                           num_parallel_tree=None,
                                           objective='multi:softprob',
                                           predictor=None, ...),
                   n_jobs=-1,
                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,
                                                             0.7],
                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],
                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,
                                                          0.25, 0.3],
                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,
                                                      15],
                                        'min_child_weight': [1, 3, 5, 7]},
                   scoring='accuracy')


GCV.best_estimator_

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.5, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=0.0, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.3, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=15, max_leaves=None,
              min_child_weight=3, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...)


GCV.best_params_

{'min_child_weight': 3,
 'max_depth': 15,
 'learning_rate': 0.3,
 'gamma': 0.0,
 'colsample_bytree': 0.5}


xgb_predict=GCV.best_estimator_.predict(x_test)


print(classification_report(y_test,xgb_predict))

              precision    recall  f1-score   support

           0       0.89      0.86      0.88        59
           1       0.96      0.90      0.93        49
           2       0.83      0.83      0.83        36
           3       0.93      1.00      0.96        53
           4       0.98      1.00      0.99        61

    accuracy                           0.93       258
   macro avg       0.92      0.92      0.92       258
weighted avg       0.93      0.93      0.93       258


confusion_matrix(y_test,xgb_predict)

array([[51,  1,  3,  4,  0],
       [ 2, 44,  3,  0,  0],
       [ 4,  1, 30,  0,  1],
       [ 0,  0,  0, 53,  0],
       [ 0,  0,  0,  0, 61]], dtype=int64)


sns.heatmap(confusion_matrix(y_test,xgb_predict),annot=True,fmt='d')

<AxesSubplot:>


accuracy_score(y_test,xgb_predict)*100

92.63565891472868


y_pred_prob=GCV.predict_proba(x_test)
y_pred_prob

array([[1.3316079e-04, 1.4766480e-03, 4.7268691e-03, 2.6481887e-04,
        9.9339855e-01],
       [1.7658512e-03, 1.8155719e-03, 9.9225992e-01, 3.7373162e-03,
        4.2135123e-04],
       [1.4803427e-02, 6.0548531e-03, 2.8830727e-03, 8.0865400e-05,
        9.7617775e-01],
       ...,
       [2.5299259e-02, 4.4000108e-02, 9.2845052e-01, 4.7666061e-04,
        1.7734175e-03],
       [9.9485695e-01, 1.0733516e-03, 1.2106402e-03, 2.7829804e-03,
        7.6079552e-05],
       [3.4951095e-03, 9.9122214e-01, 7.4557174e-04, 1.6014204e-04,
        4.3769712e-03]], dtype=float32)


from sklearn.metrics import roc_auc_score,roc_curve,plot_roc_curve


roc_auc_score(y_test,y_pred_prob,multi_class='ovr', average='weighted')

0.9959487409364643


# classes' auc-roc curve

fpr={}
tpr={}
threshold={}

n_class=5

for i in range(n_class):
    fpr[i],tpr[i],threshold[i]=roc_curve(y_test,y_pred_prob[:,i],pos_label=i)
    
#plotting
plt.plot(fpr[0],tpr[0],linestyle='--', color='orange',label='Class 0 vs rest'  )
plt.plot(fpr[1],tpr[1],linestyle='--', color='green',label='Class 1 vs rest'  )
plt.plot(fpr[2],tpr[2],linestyle='--', color='yellow',label='Class 2 vs rest'  )
plt.plot(fpr[3],tpr[3],linestyle='--', color='red',label='Class 3 vs rest'  )
plt.plot(fpr[4],tpr[4],linestyle='--', color='blue',label='Class 4 vs rest'  )
plt.title('Multi class ROC Curve')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.legend(loc='best')
plt.show()


import joblib


joblib.dump(GCV.best_estimator_,'Global_Power_Fuel_prediction.pkl')

['Global_Power_Fuel_prediction.pkl']


df.head()


data=df.copy()


data.isnull().sum()

capacity_mw            0
latitude               0
longitude              0
primary_fuel           0
commissioning_year     0
generation_gwh_2014    0
generation_gwh_2015    0
generation_gwh_2016    0
generation_gwh_2017    0
generation_gwh_2018    0
dtype: int64


#additionally filled in upper stage misisng values


data['Total']=df['generation_gwh_2014']+df['generation_gwh_2015']+df['generation_gwh_2016']+df['generation_gwh_2017']+df['generation_gwh_2018']


data


data.drop(['generation_gwh_2014','generation_gwh_2015','generation_gwh_2016','generation_gwh_2017','generation_gwh_2018'],axis=1,inplace=True)


data.head()


sns.heatmap(data.corr(),annot=True)

<AxesSubplot:>


col=['capacity_mw','Total']


for i in col:
    sns.boxplot(data[i])
    plt.show()


for i in col:
    IQR= data[i].quantile(.75)-data[i].quantile(.25)
    upper= (data[i].quantile(.75) +(1.5* IQR))
    data[i]=np.where(data[i]>upper,upper,data[i])


for i in col:
    sns.boxplot(data[i])
    plt.show()


data.head()


inp=data.drop('capacity_mw', axis=1)
out=data['capacity_mw']


inp.shape , out.shape

((607, 5), (607,))


inp.head()


inp.skew()

latitude             -0.058579
longitude             0.986666
commissioning_year   -1.198052
Total                 1.315095
dtype: float64


v=inp.drop('primary_fuel',axis=1)


from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
scaled=sc.fit_transform(v)
scaled

array([[ 0.53608541, -0.77608434,  0.91147876,  1.00628113],
       [ 0.39288959,  2.50590045,  0.26575529, -0.63446469],
       [ 0.08718866,  0.75215301,  0.91147876,  1.31200528],
       ...,
       [-1.32776397, -0.1932456 , -0.37996817, -0.92676426],
       [-0.81914763, -0.23772065,  0.97018089, -0.40252899],
       [ 0.4694868 , -0.94483485,  0.91147876,  1.00628113]])


from statsmodels.stats.outliers_influence import variance_inflation_factor
VIF= pd.DataFrame()
VIF['features']=v.columns


VIF['vif']= [variance_inflation_factor(scaled,i) for i in range(len(v.columns))]

VIF


# 3 Controlled multicolinearity


inp.head(2)


dummied=pd.get_dummies(inp, drop_first=True)


from sklearn.preprocessing import power_transform
transform=power_transform(dummied)


from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
standard=sc.fit_transform(transform)


standard.shape , out.shape

((607, 8), (607,))


#It is a regression issue because the output variable's average price has continuous data.
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error

LR= LinearRegression()


# Finding the best random_state for the model during the train-test split

for i in range(0,200):
    x_train,x_test,y_train,y_test= train_test_split(standard,out,random_state=i,test_size=.3)
    LR.fit(x_train,y_train)
    train_pred=LR.predict(x_train)
    test_pred=LR.predict(x_test)
    if round(r2_score(y_test,test_pred),2)==round(r2_score(y_train,train_pred),2):
        print("At random state ", i, "The model performance very well")
        print("At random state: ",i)
        print("Test R2 score is: ", round(r2_score(y_test,test_pred),2))
        print('Train R2 score is: ', round(r2_score(y_train,train_pred),2))
        print('X'*50,'\n')

At random state  8 The model performance very well
At random state:  8
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  17 The model performance very well
At random state:  17
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  22 The model performance very well
At random state:  22
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  25 The model performance very well
At random state:  25
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  38 The model performance very well
At random state:  38
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  44 The model performance very well
At random state:  44
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  86 The model performance very well
At random state:  86
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  97 The model performance very well
At random state:  97
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  102 The model performance very well
At random state:  102
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  155 The model performance very well
At random state:  155
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  156 The model performance very well
At random state:  156
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  158 The model performance very well
At random state:  158
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  161 The model performance very well
At random state:  161
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  182 The model performance very well
At random state:  182
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  189 The model performance very well
At random state:  189
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 

At random state  195 The model performance very well
At random state:  195
Test R2 score is:  0.39
Train R2 score is:  0.39
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX


# Random State number 44 .


x_train,x_test,y_train,y_test= train_test_split(standard,out,random_state=44,test_size=.2)
LR.fit(x_train,y_train)

LinearRegression()


from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge, Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import SGDRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor


LR_model= LinearRegression()
RD_model= Ridge()
LS_model= Lasso()
DT_model= DecisionTreeRegressor()
SV_model= SVR()
KNR_model= KNeighborsRegressor()
RFR_model= RandomForestRegressor()
XGB_model= XGBRegressor()
Elastic_model= ElasticNet()
SGH_model= SGDRegressor()
Bag_model=BaggingRegressor()
ADA_model=AdaBoostRegressor()
GB_model= GradientBoostingRegressor()

model=[LR_model,RD_model,LS_model,DT_model,SV_model,KNR_model,RFR_model,XGB_model,Elastic_model,SGH_model,Bag_model,ADA_model,GB_model ]


for m in model:
    m.fit(x_train,y_train)
    print('mean_absolute_error of ',m ,'model', mean_absolute_error(y_test,m.predict(x_test)))
    print('mean_square_error of',m,'model' , mean_squared_error(y_test,m.predict(x_test)))
    print('R2 Score of',m,'model', r2_score(y_test,m.predict(x_test) )*100)
    print('X' * 50, '\n\n')

mean_absolute_error of  LinearRegression() model 264.8930605103229
mean_square_error of LinearRegression() model 128645.41768614174
R2 Score of LinearRegression() model 39.27971970310852
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  Ridge() model 264.9655942674523
mean_square_error of Ridge() model 128666.43799660975
R2 Score of Ridge() model 39.269798174876094
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  Lasso() model 264.9012550849401
mean_square_error of Lasso() model 128798.33570679805
R2 Score of Lasso() model 39.20754282153974
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  DecisionTreeRegressor() model 140.99401639344262
mean_square_error of DecisionTreeRegressor() model 63609.91194075411
R2 Score of DecisionTreeRegressor() model 69.97629801221244
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  SVR() model 324.3736007248969
mean_square_error of SVR() model 237460.53747989514
R2 Score of SVR() model -12.080714996061491
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  KNeighborsRegressor() model 165.2894245901639
mean_square_error of KNeighborsRegressor() model 76996.73324519048
R2 Score of KNeighborsRegressor() model 63.65775549037228
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  RandomForestRegressor() model 121.60888797814209
mean_square_error of RandomForestRegressor() model 41387.81940775061
R2 Score of RandomForestRegressor() model 80.4650640456783
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...) model 128.34053569193063
mean_square_error of XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...) model 44688.59650091947
R2 Score of XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...) model 78.90710641376522
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  ElasticNet() model 286.0568805766707
mean_square_error of ElasticNet() model 137740.9384019941
R2 Score of ElasticNet() model 34.98665915539315
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  SGDRegressor() model 264.6366785206043
mean_square_error of SGDRegressor() model 128751.41544346679
R2 Score of SGDRegressor() model 39.22968905567945
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  BaggingRegressor() model 126.56472158469944
mean_square_error of BaggingRegressor() model 42678.60967488723
R2 Score of BaggingRegressor() model 79.85581462012743
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  AdaBoostRegressor() model 192.86387809515398
mean_square_error of AdaBoostRegressor() model 55895.65946950447
R2 Score of AdaBoostRegressor() model 73.6174037799908
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 


mean_absolute_error of  GradientBoostingRegressor() model 130.07431736322366
mean_square_error of GradientBoostingRegressor() model 36493.2668319392
R2 Score of GradientBoostingRegressor() model 82.77527928440699
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX


from sklearn.model_selection import cross_val_score


for i in model:
    print('mean_square of ',i, 'model',mean_squared_error(y_test,i.predict(x_test)))
    print("cross Validation score of ",i ," is ",cross_val_score(i,standard,out,cv=10, scoring='neg_mean_squared_error').mean())
    print('*'*50)

mean_square of  LinearRegression() model 128645.41768614174
cross Validation score of  LinearRegression()  is  -143780.04095285514
**************************************************
mean_square of  Ridge() model 128666.43799660975
cross Validation score of  Ridge()  is  -143758.43453657435
**************************************************
mean_square of  Lasso() model 128798.33570679805
cross Validation score of  Lasso()  is  -143587.50585740217
**************************************************
mean_square of  DecisionTreeRegressor() model 63609.91194075411
cross Validation score of  DecisionTreeRegressor()  is  -79625.96326109182
**************************************************
mean_square of  SVR() model 237460.53747989514
cross Validation score of  SVR()  is  -265864.62185436086
**************************************************
mean_square of  KNeighborsRegressor() model 76996.73324519048
cross Validation score of  KNeighborsRegressor()  is  -109724.62242995563
**************************************************
mean_square of  RandomForestRegressor() model 41387.81940775061
cross Validation score of  RandomForestRegressor()  is  -45077.62639240614
**************************************************
mean_square of  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...) model 44688.59650091947
cross Validation score of  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...)  is  -51771.37845471575
**************************************************
mean_square of  ElasticNet() model 137740.9384019941
cross Validation score of  ElasticNet()  is  -150556.21358762536
**************************************************
mean_square of  SGDRegressor() model 128751.41544346679
cross Validation score of  SGDRegressor()  is  -143784.40641704988
**************************************************
mean_square of  BaggingRegressor() model 42678.60967488723
cross Validation score of  BaggingRegressor()  is  -49763.20175897897
**************************************************
mean_square of  AdaBoostRegressor() model 55895.65946950447
cross Validation score of  AdaBoostRegressor()  is  -66574.19803662592
**************************************************
mean_square of  GradientBoostingRegressor() model 36493.2668319392
cross Validation score of  GradientBoostingRegressor()  is  -49250.841263385446
**************************************************


import numpy as np
from sklearn.model_selection import RandomizedSearchCV

n_estimator= [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]  # the default number of trees in a random forest is 100
max_features= ['auto','sqrt','log2']  # fewest attributes required to design a decision tree
max_depth=[10, 64, 118, 173, 227, 282, 336, 391, 445, 500]  # Maximum decision tree depth
min_samples_split= [1,2,3]   # Minimal number of samples needed to split the node
min_samples_leaf= [1,3,4,6,7,9]  #each leaf node requires a minimum number of samples.

random_grid= {'n_estimators': n_estimator,
             'max_features':max_features,
             'max_depth':max_depth,
             'min_samples_split':min_samples_split,
             'min_samples_leaf':min_samples_leaf,
             }
random_grid

{'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000],
 'max_features': ['auto', 'sqrt', 'log2'],
 'max_depth': [10, 64, 118, 173, 227, 282, 336, 391, 445, 500],
 'min_samples_split': [1, 2, 3],
 'min_samples_leaf': [1, 3, 4, 6, 7, 9]}


randomCV =RandomizedSearchCV(RFR_model,param_distributions=random_grid,cv=5,random_state=100,verbose=2,n_jobs=-1)
randomCV.fit(x_train,y_train)

Fitting 5 folds for each of 10 candidates, totalling 50 fits

RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=-1,
                   param_distributions={'max_depth': [10, 64, 118, 173, 227,
                                                      282, 336, 391, 445, 500],
                                        'max_features': ['auto', 'sqrt',
                                                         'log2'],
                                        'min_samples_leaf': [1, 3, 4, 6, 7, 9],
                                        'min_samples_split': [1, 2, 3],
                                        'n_estimators': [200, 400, 600, 800,
                                                         1000, 1200, 1400, 1600,
                                                         1800, 2000]},
                   random_state=100, verbose=2)


randomCV.best_estimator_

RandomForestRegressor(max_depth=445, min_samples_leaf=3, min_samples_split=3,
                      n_estimators=1400)


randomCV.best_params_

{'n_estimators': 1400,
 'min_samples_split': 3,
 'min_samples_leaf': 3,
 'max_features': 'auto',
 'max_depth': 445}


random_predict=randomCV.best_estimator_.predict(x_test)


print('mean_square_error of GrieSearchCV model' , mean_squared_error(y_test,random_predict))
print('R2 Score of Grid Search CV model', r2_score(y_test,random_predict )*100)

mean_square_error of GrieSearchCV model 37606.34510836825
R2 Score of Grid Search CV model 82.2499094255128


# Square Root Error is :

np.sqrt(mean_squared_error(y_test,random_predict))

193.9235548054136


import joblib


joblib.dump(randomCV.best_estimator_,'Global_Power_capacity_prediction.pkl')

['Global_Power_capacity_prediction.pkl']

	capacity_mw	latitude	longitude	commissioning_year	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018
capacity_mw	1.000000	0.051309	0.197129	0.304087	0.839094	0.844352	0.870620	0.886673	0.901918
latitude	0.051309	1.000000	0.036362	0.129902	0.060330	0.048610	0.041387	0.040568	0.040476
longitude	0.197129	0.036362	1.000000	0.121910	0.013511	0.022760	0.050732	0.048678	0.045599
commissioning_year	0.304087	0.129902	0.121910	1.000000	0.160014	0.170363	0.193743	0.191367	0.193694
generation_gwh_2014	0.839094	0.060330	0.013511	0.160014	1.000000	0.961098	0.937060	0.912561	0.902781
generation_gwh_2015	0.844352	0.048610	0.022760	0.170363	0.961098	1.000000	0.974918	0.940191	0.934371
generation_gwh_2016	0.870620	0.041387	0.050732	0.193743	0.937060	0.974918	1.000000	0.972024	0.962073
generation_gwh_2017	0.886673	0.040568	0.048678	0.191367	0.912561	0.940191	0.972024	1.000000	0.985856
generation_gwh_2018	0.901918	0.040476	0.045599	0.193694	0.902781	0.934371	0.962073	0.985856	1.000000

	capacity_mw	latitude	longitude	primary_fuel	commissioning_year	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018
1	98.0	24.7663	74.6090	Coal	2015.0	4737.279310	4649.585751	4761.233946	4870.537285	5036.420635
3	135.0	23.8712	91.3602	Gas	2004.0	617.789264	843.747000	886.004428	663.774500	626.239128
4	1800.0	21.9603	82.4091	Coal	2015.0	3035.550000	5916.370000	6243.000000	5385.579736	7279.000000
5	250.0	23.7689	68.6447	Coal	2005.0	1153.421000	1208.852000	1175.765000	1147.913000	976.655000
6	60.0	10.4547	77.0078	Hydro	1970.0	157.558250	152.195200	61.421350	89.629600	48.327150

	capacity_mw	latitude	longitude	commissioning_year	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018
1	98.0	24.7663	74.6090	2015.0	4737.279310	4649.585751	4761.233946	4870.537285	5036.420635
3	135.0	23.8712	91.3602	2004.0	617.789264	843.747000	886.004428	663.774500	626.239128
4	1800.0	21.9603	82.4091	2015.0	3035.550000	5916.370000	6243.000000	5385.579736	7279.000000
5	250.0	23.7689	68.6447	2005.0	1153.421000	1208.852000	1175.765000	1147.913000	976.655000
6	60.0	10.4547	77.0078	1970.0	157.558250	152.195200	61.421350	89.629600	48.327150

	capacity_mw	latitude	longitude	commissioning_year	Total
1	98.0	24.7663	74.6090	2015.0	24055.056927
3	135.0	23.8712	91.3602	2004.0	3637.554320
4	1453.5	21.9603	82.4091	2015.0	27859.499736
5	250.0	23.7689	68.6447	2005.0	5662.606000
6	60.0	10.4547	77.0078	1970.0	509.131550
...	...	...	...	...	...
893	62.4	30.3033	77.5684	1991.0	984.064550
899	600.0	30.1081	77.3257	2007.0	16223.198930
901	106.6	13.1156	77.5838	1993.0	0.167471
902	1453.5	16.2949	77.3568	2016.0	6523.770835
905	80.0	24.3500	73.7477	2015.0	24055.056927

	capacity_mw	latitude	longitude	primary_fuel	commissioning_year	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018
1	98.0	24.7663	74.6090	Coal	2015.0	4737.279310	4649.585751	4761.233946	4870.537285	5036.420635
3	135.0	23.8712	91.3602	Gas	2004.0	617.789264	843.747000	886.004428	663.774500	626.239128
4	1800.0	21.9603	82.4091	Coal	2015.0	3035.550000	5916.370000	6243.000000	5385.579736	7279.000000
5	250.0	23.7689	68.6447	Coal	2005.0	1153.421000	1208.852000	1175.765000	1147.913000	976.655000
6	60.0	10.4547	77.0078	Hydro	1970.0	157.558250	152.195200	61.421350	89.629600	48.327150

About Dataset¶

Absent Values¶

Removing all Missing Attributes.¶

Univariant EDA¶

Predicting about Primary_fuel¶

This information is not balanced.¶

Bivariant Exploratory Data Analysis¶

Biomass, solar, and wind-generated gwh are not available for the years 2014, 2015, 2016, 2017, or 2018.¶

Filling up Missing Values¶

Target Variable for Encoding¶

Distinguishing between independent and dependent attributes¶

Outliers¶

Our new feature, "Total," which we have added during the entire year.¶

VIF¶

Target Variable Encoding¶

Transforming¶

Data Balancing¶

Use of ML(Machine Learning)¶

Cross Validation¶

Hypertuning¶

Accuracy increased to 92.¶

A 99% ROC_AUC score is considered to be very good.¶

A classification model to be saved¶

Making a capacity_mw prediction.¶

Managing the outlier¶

Distinguishing between independent and dependent properties.¶

Doing a Multicolinearity Check¶

Categorical Variable Encoding¶

Transforming¶

Standardizing¶

Using ML(Machine Learning)¶

Cross Validating¶

Hypertuning¶

Model Saving¶

	country	country_long	name	gppd_idnr	capacity_mw	latitude	longitude	primary_fuel	other_fuel1	other_fuel2	...	year_of_capacity_data	generation_gwh_2013	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018	generation_gwh_2019	generation_data_source	estimated_generation_gwh
0	IND	India	ACME Solar Tower	WRI1020239	2.5	28.1839	73.2407	Solar	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	IND	India	ADITYA CEMENT WORKS	WRI1019881	98.0	24.7663	74.6090	Coal	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	IND	India	AES Saurashtra Windfarms	WRI1026669	39.2	21.9038	69.3732	Wind	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	IND	India	AGARTALA GT	IND0000001	135.0	23.8712	91.3602	Gas	NaN	NaN	...	2019.0	NaN	617.789264	843.747	886.004428	663.774500	626.239128	NaN	Central Electricity Authority	NaN
4	IND	India	AKALTARA TPP	IND0000002	1800.0	21.9603	82.4091	Coal	Oil	NaN	...	2019.0	NaN	3035.550000	5916.370	6243.000000	5385.579736	7279.000000	NaN	Central Electricity Authority	NaN

	capacity_mw	latitude	longitude	other_fuel3	commissioning_year	wepp_id	year_of_capacity_data	generation_gwh_2013	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018	generation_gwh_2019	estimated_generation_gwh
count	907.000000	861.000000	861.000000	0.0	527.000000	0.0	519.0	0.0	398.000000	422.000000	434.000000	440.000000	448.000000	0.0	0.0
mean	326.223755	21.197918	77.464907	NaN	1997.091082	NaN	2019.0	NaN	2431.823590	2428.226946	2467.936859	2547.759305	2600.804099	NaN	NaN
std	590.085456	6.239612	4.939316	NaN	17.082868	NaN	0.0	NaN	4026.440035	4194.596959	4162.884308	4196.991169	4314.880456	NaN	NaN
min	0.000000	8.168900	68.644700	NaN	1927.000000	NaN	2019.0	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN
25%	16.725000	16.773900	74.256200	NaN	1988.000000	NaN	2019.0	NaN	223.557672	176.381062	188.285252	177.874930	193.378250	NaN	NaN
50%	59.200000	21.780000	76.719500	NaN	2001.000000	NaN	2019.0	NaN	801.123775	711.181225	737.205450	817.977250	751.644375	NaN	NaN
75%	385.250000	25.512400	79.440800	NaN	2012.000000	NaN	2019.0	NaN	3035.306250	3084.121250	3282.861313	3275.690475	3143.535900	NaN	NaN
max	4760.000000	34.649000	95.408000	NaN	2018.000000	NaN	2019.0	NaN	28127.000000	30539.000000	30015.000000	35116.000000	35136.000000	NaN	NaN

	country	country_long	name	gppd_idnr	capacity_mw	latitude	longitude	primary_fuel	other_fuel1	other_fuel2	other_fuel3	commissioning_year	owner	source	url	geolocation_source	wepp_id	year_of_capacity_data	generation_gwh_2013	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018	generation_gwh_2019	generation_data_source	estimated_generation_gwh
14	IND	India	ANOOPGARH ST I&II	IND0000012	9.00	NaN	NaN	Hydro	NaN	NaN	NaN	1987.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
25	IND	India	Abhijeet	WRI1020238	50.00	NaN	NaN	Solar	NaN	NaN	NaN	2015.0	Ener-t International Ltd.	National Renewable Energy Laboratory	http://www.nrel.gov/csp/solarpaces/project_det...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
42	IND	India	BAGLIHAR HEP-II	IND0000021	450.00	NaN	NaN	Hydro	NaN	NaN	NaN	2015.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	55.3220	1750.18510	1812.84025	1848.620450	NaN	Central Electricity Authority	NaN
45	IND	India	BALCO TPP	IND0000025	600.00	NaN	NaN	Coal	Oil	NaN	NaN	2015.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	1437.9547	2800.00000	2120.68000	2553.000000	NaN	Central Electricity Authority	NaN
67	IND	India	BCPL Wind Farm	WRI1026675	5.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Bharat Petroleum Corporation Limited	CDM	https://cdm.unfccc.int/Projects/DB/DNV-CUK1218...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
72	IND	India	BELLARY DG	IND0000044	25.20	NaN	NaN	Oil	NaN	NaN	NaN	2000.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	0.00	0.0000	0.00000	0.00000	0.000000	NaN	Central Electricity Authority	NaN
178	IND	India	DIMBE	IND0000102	5.00	NaN	NaN	Hydro	NaN	NaN	NaN	1997.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
192	IND	India	Davangere Wind Farm	WRI1026100	125.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	NaN	CDM	https://cdm.unfccc.int/Projects/DB/DNV-CUK1142...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
201	IND	India	E.G. CANAL	IND0000110	15.00	NaN	NaN	Hydro	NaN	NaN	NaN	1996.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
208	IND	India	Essel Mining Wind Farm	WRI1026101	75.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	NaN	CDM	https://cdm.unfccc.int/filestorage/O/6/L/O6LOW...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
250	IND	India	Gujurat Solar On	WRI1020243	28.00	NaN	NaN	Solar	NaN	NaN	NaN	2014.0	Cargo Solar Power	National Renewable Energy Laboratory	http://www.nrel.gov/csp/solarpaces/project_det...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
329	IND	India	KASHANG INTEGRATED	IND0000517	195.00	NaN	NaN	Hydro	NaN	NaN	NaN	2016.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	55.80955	196.14435	117.648800	NaN	Central Electricity Authority	NaN
330	IND	India	KASHIPUR CCPP	IND0000518	225.00	NaN	NaN	Gas	NaN	NaN	NaN	2016.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	462.28260	1312.40000	815.984131	NaN	Central Electricity Authority	NaN
332	IND	India	KATGHORA TPP	IND0000187	35.00	NaN	NaN	Coal	Oil	NaN	NaN	2012.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	0.00	0.0000	0.00000	0.00000	0.000000	NaN	Central Electricity Authority	NaN
386	IND	India	KVK Energy Solar Project	WRI1020244	100.00	NaN	NaN	Solar	NaN	NaN	NaN	2013.0	KVK Energy Ventures Ltd	National Renewable Energy Laboratory	http://www.nrel.gov/csp/solarpaces/project_det...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
420	IND	India	LEIMAKHONG DG	IND0000231	36.00	NaN	NaN	Oil	NaN	NaN	NaN	1999.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	0.00	0.0000	0.00000	0.00000	0.000000	NaN	Central Electricity Authority	NaN
422	IND	India	LIKIM RO	IND0000233	24.00	NaN	NaN	Hydro	NaN	NaN	NaN	2001.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
430	IND	India	LOWER LAGYAP	IND0000237	12.00	NaN	NaN	Hydro	NaN	NaN	NaN	1979.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
435	IND	India	LVS POWER DG	IND0000241	36.80	NaN	NaN	Oil	NaN	NaN	NaN	2000.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	0.00	0.0000	0.00000	0.00000	0.000000	NaN	Central Electricity Authority	NaN
440	IND	India	Landewadi Wind Farm	WRI1026790	4.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Serum Institute of India Limited	CDM	https://cdm.unfccc.int/Projects/DB/LRQA%20Ltd1...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
466	IND	India	MAQSOODPUR IPP	IND0000259	90.00	NaN	NaN	Coal	Oil	NaN	NaN	2011.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	473.91	281.7300	378.68000	150.95200	126.494100	NaN	Central Electricity Authority	NaN
478	IND	India	MOHAMAD PUR	IND0000270	9.30	NaN	NaN	Hydro	NaN	NaN	NaN	1949.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
479	IND	India	MOHARA	IND0000271	9.00	NaN	NaN	Hydro	NaN	NaN	NaN	1962.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
483	IND	India	MOYAGCHU	IND0000274	4.00	NaN	NaN	Hydro	NaN	NaN	NaN	1993.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
502	IND	India	Megha Solar Plant	WRI1020245	50.00	NaN	NaN	Solar	NaN	NaN	NaN	2014.0	Megha Engineering and Infrastructure	National Renewable Energy Laboratory	http://www.nrel.gov/csp/solarpaces/project_det...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
538	IND	India	NIRGAJANI(Ganga Canal)	IND0000304	5.00	NaN	NaN	Hydro	NaN	NaN	NaN	1937.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
540	IND	India	NIWARI TPP	IND0000305	45.00	NaN	NaN	Coal	Oil	NaN	NaN	2013.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	298.28	124.9248	208.78880	77.33440	68.094400	NaN	Central Electricity Authority	NaN
545	IND	India	NSL Wind Farm	WRI1026676	27.65	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Nuziveedu Seeds limited	CDM	https://cdm.unfccc.int/Projects/DB/DNV-CUK1173...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
655	IND	India	RSMNL Wind Farm	WRI1026679	22.50	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Rajasthan State Mines and Minerals Limited	CDM	https://cdm.unfccc.int/Projects/DB/BVQI1201770...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
674	IND	India	Ruchi Soya Palsodi Wind Farm	WRI1026678	22.50	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Rucha Soya Industries Limited	CDM	https://cdm.unfccc.int/Projects/DB/RWTUV128515...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
680	IND	India	SAINJ	IND0000534	100.00	NaN	NaN	Hydro	NaN	NaN	NaN	2017.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	134.31505	406.765950	NaN	Central Electricity Authority	NaN
705	IND	India	SHAHPUR	IND0000402	6.60	NaN	NaN	Hydro	NaN	NaN	NaN	1997.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
711	IND	India	SHIRPUR	IND0000535	150.00	NaN	NaN	Coal	Oil	NaN	NaN	2017.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	4.31340	0.000000	NaN	Central Electricity Authority	NaN
712	IND	India	SHIVAPURA	IND0000407	18.00	NaN	NaN	Hydro	NaN	NaN	NaN	1992.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
731	IND	India	SONE EAST CANAL	IND0000419	3.30	NaN	NaN	Hydro	NaN	NaN	NaN	1996.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
732	IND	India	SONE WEST CANAL	IND0000420	6.60	NaN	NaN	Hydro	NaN	NaN	NaN	1993.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
741	IND	India	SURAT GARH	IND0000429	4.00	NaN	NaN	Hydro	NaN	NaN	NaN	1992.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
770	IND	India	Sivalaperi Wind Farm	WRI1026677	24.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Green Infra Wind Power Projects Limited	CDM	https://cdm.unfccc.int/Projects/DB/RWTUV135219...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
773	IND	India	Sterling Agro Akal Wind Farm	WRI1026754	3.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Sterling Agro Industries ltd.	CDM	https://cdm.unfccc.int/Projects/DB/SGS-UKL1333...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
774	IND	India	Sterling Agro Mokla Wind Farm	WRI1026755	6.30	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Sterling Agro Industries ltd.	CDM	https://cdm.unfccc.int/Projects/DB/SGS-UKL1333...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
775	IND	India	Sterling Karnataka site	WRI1026756	6.25	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Sterling Agro Industries ltd.	CDM	https://cdm.unfccc.int/Projects/DB/SGS-UKL1333...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
776	IND	India	Sterling Madhya Pradesh site	WRI1026757	6.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	Sterling Agro Industries ltd.	CDM	https://cdm.unfccc.int/Projects/DB/SGS-UKL1333...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
784	IND	India	TAGO	IND0000437	4.50	NaN	NaN	Hydro	NaN	NaN	NaN	1991.0	NaN	Central Electricity Authority	http://www.cea.nic.in/	WRI	NaN	2019.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
828	IND	India	Tamilnadu Wind Farm	WRI1026102	74.00	NaN	NaN	Wind	NaN	NaN	NaN	NaN	NaN	CDM	https://cdm.unfccc.int/filestorage/2/F/R/2FRTL...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
884	IND	India	VRL Wind Farm	WRI1026103	42.50	NaN	NaN	Wind	NaN	NaN	NaN	NaN	NaN	CDM	https://cdm.unfccc.int/Projects/DB/SGS-UKL1225...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	count	mean	std	min	25%	50%	75%	max
capacity_mw	907.0	326.223755	590.085456	0.0000	16.725000	59.200000	385.250000	4760.000
latitude	861.0	21.197918	6.239612	8.1689	16.773900	21.780000	25.512400	34.649
longitude	861.0	77.464907	4.939316	68.6447	74.256200	76.719500	79.440800	95.408
other_fuel3	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
commissioning_year	527.0	1997.091082	17.082868	1927.0000	1988.000000	2001.000000	2012.000000	2018.000
wepp_id	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
year_of_capacity_data	519.0	2019.000000	0.000000	2019.0000	2019.000000	2019.000000	2019.000000	2019.000
generation_gwh_2013	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
generation_gwh_2014	398.0	2431.823590	4026.440035	0.0000	223.557672	801.123775	3035.306250	28127.000
generation_gwh_2015	422.0	2428.226946	4194.596959	0.0000	176.381062	711.181225	3084.121250	30539.000
generation_gwh_2016	434.0	2467.936859	4162.884308	0.0000	188.285252	737.205450	3282.861313	30015.000
generation_gwh_2017	440.0	2547.759305	4196.991169	0.0000	177.874930	817.977250	3275.690475	35116.000
generation_gwh_2018	448.0	2600.804099	4314.880456	0.0000	193.378250	751.644375	3143.535900	35136.000
generation_gwh_2019	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
estimated_generation_gwh	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	capacity_mw	latitude	longitude	primary_fuel	commissioning_year	owner	source	geolocation_source	year_of_capacity_data	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018	generation_data_source
0	2.50	28.1839	73.2407	Solar	2011.0	Solar Paces	National Renewable Energy Laboratory	National Renewable Energy Laboratory	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	98.00	24.7663	74.6090	Coal	NaN	Ultratech Cement ltd	Ultratech Cement ltd	WRI	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	39.20	21.9038	69.3732	Wind	NaN	AES	CDM	WRI	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	135.00	23.8712	91.3602	Gas	2004.0	NaN	Central Electricity Authority	WRI	2019.0	617.789264	843.7470	886.004428	663.774500	626.239128	Central Electricity Authority
4	1800.00	21.9603	82.4091	Coal	2015.0	NaN	Central Electricity Authority	WRI	2019.0	3035.550000	5916.3700	6243.000000	5385.579736	7279.000000	Central Electricity Authority
5	250.00	23.7689	68.6447	Coal	2005.0	NaN	Central Electricity Authority	WRI	2019.0	1153.421000	1208.8520	1175.765000	1147.913000	976.655000	Central Electricity Authority
6	60.00	10.4547	77.0078	Hydro	1970.0	NaN	Central Electricity Authority	WRI	2019.0	157.558250	152.1952	61.421350	89.629600	48.327150	Central Electricity Authority
7	192.00	32.2258	77.2070	Hydro	2010.0	NaN	Central Electricity Authority	WRI	2019.0	674.391100	721.3352	675.724400	679.594950	579.318850	Central Electricity Authority
8	290.00	16.3300	75.8863	Hydro	2004.0	NaN	Central Electricity Authority	WRI	2019.0	480.594950	144.4342	402.029750	439.372100	406.377900	Central Electricity Authority
9	210.00	23.1642	81.6373	Coal	2008.0	NaN	Central Electricity Authority	WRI	2019.0	1887.904000	1643.0460	1338.093000	1563.457000	1487.880000	Central Electricity Authority
10	1350.00	21.0782	77.9009	Coal	2014.0	NaN	Central Electricity Authority	WRI	2019.0	1920.971000	5629.6630	1701.008000	4350.558000	3717.154000	Central Electricity Authority
11	134.00	31.2717	76.4938	Hydro	1985.0	NaN	Central Electricity Authority	WRI	2019.0	614.412500	665.1973	670.500650	644.570950	425.641100	Central Electricity Authority
12	1200.00	24.2007	82.8000	Coal	2011.0	NaN	Central Electricity Authority	WRI	2019.0	7744.000000	8076.8105	7902.021600	7940.740000	7873.973000	Central Electricity Authority
13	16.95	31.2412	77.8769	Hydro	1986.0	NaN	Central Electricity Authority	WRI	2019.0	NaN	NaN	NaN	NaN	NaN	NaN
14	9.00	NaN	NaN	Hydro	1987.0	NaN	Central Electricity Authority	WRI	2019.0	NaN	NaN	NaN	NaN	NaN	NaN

	primary_fuel	capacity_mw
0	Biomass	20.065200
1	Coal	797.826434
2	Gas	364.818928
3	Hydro	185.026972
4	Nuclear	975.555556
5	Oil	88.942000
6	Solar	21.712598
7	Wind	33.429675

	capacity_mw	latitude	longitude	primary_fuel	commissioning_year	generation_gwh_2014	generation_gwh_2015	generation_gwh_2016	generation_gwh_2017	generation_gwh_2018
19	10.00	18.0845	76.1851	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
20	17.00	19.5867	74.7061	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
21	24.00	16.7708	74.9191	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
40	28.00	16.2804	75.2250	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
70	22.80	16.6505	74.8209	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
71	15.00	16.2774	74.7111	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
80	10.00	21.0813	79.7708	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
94	10.00	25.2267	74.6216	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
152	24.00	16.5471	77.0825	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
164	30.00	18.4271	74.6338	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
165	24.00	14.3277	75.8808	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
180	12.96	19.4496	75.0382	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
185	22.00	14.6284	75.8448	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
255	24.00	16.6038	74.4045	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
257	25.50	17.1202	76.4004	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
260	18.90	19.2829	77.1384	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
262	25.00	13.9808	76.6279	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
286	50.00	27.6010	72.2242	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
321	16.00	17.1657	74.2459	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
337	18.00	14.7753	75.3131	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
347	26.00	12.4806	77.0411	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
354	20.00	16.0327	74.4583	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
355	20.00	16.5890	74.2993	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
356	17.50	16.6890	74.1201	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
357	16.00	12.1056	76.7606	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
361	26.00	12.7020	76.9787	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
379	16.50	16.2651	75.5302	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
426	31.50	17.4462	75.7161	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
450	15.00	20.8558	78.5814	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
496	11.20	15.8555	75.1002	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
516	10.00	21.0582	79.4850	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
517	18.90	20.8062	79.0548	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
520	10.00	22.9649	78.8132	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
576	30.00	18.9057	76.7282	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
633	11.00	12.7145	77.4524	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
640	12.50	18.5354	76.2450	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
679	18.00	17.7080	75.1361	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
686	24.00	16.3918	75.0411	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
687	19.70	17.1354	74.4272	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
688	10.00	16.9171	74.4821	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
689	12.50	16.8810	74.5898	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
692	26.00	16.2478	74.5003	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
698	16.00	19.6125	74.1924	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
710	36.00	16.7493	74.5854	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
716	22.00	15.6933	74.7162	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
717	20.00	17.8676	74.8405	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
730	16.00	19.3804	74.8397	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
819	28.00	15.6609	76.8756	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
874	19.80	17.7382	75.3218	Biomass	NaN	NaN	NaN	NaN	NaN	NaN
900	16.00	17.1653	74.2456	Biomass	NaN	NaN	NaN	NaN	NaN	NaN

	features	vif
0	capacity_mw	1.789803
1	latitude	1.038009
2	longitude	1.043452
3	commissioning_year	1.127938
4	Total	1.888203

	features	vif
0	latitude	1.038017
1	longitude	1.030960
2	commissioning_year	1.136174
3	Total	1.130474